Data Corruption - Part 5 (Control System and Process Impacts)

Data Corruption - Part 5 (Process Dynamics and Control Strategy Impacts)

This series on Data Corruption in Control Systems explores the most common—yet frequently overlooked—problems affecting industrial control system performance and associated data & analytics.

Now, in Part 5 of our Data Corruption in Control Systems series. In previous installments, we've examined various sources of data corruption:

• Part 1 we discussed Signal Filtering issues
• Part 2 we covered Digital Resolution & Degradation problems
• Part 3 talked about Input Accuracy & Precision challenges
• Part 4 went over Mechanical Output Devices (valves, actuators, and positioners)

In part 5, we will examine how the dynamics of the process and/or the controller can introduce problems that lead to poor quality data. 

These process and control strategy issues are often more subtle than the mechanical problems discussed in Part 4, as they can create complex patterns in data that mimic legitimate process behavior. This makes them particularly challenging to identify and address, especially when using data analytics tools that aren't aware of intricate details or relationships of some of the data points or their relationships. 

Why Process Dynamics and Control Strategies Corrupt Data

Process dynamics and controller responses can produce data errors by creating process disturbances and variations, nonlinearities, or discontinuities in response. This can happen via several different mechanisms, such as:

1. Process valve reaction problems
2. Controller setup and tuning issues
3. Process dynamics and nonlinearity issues
4. Control strategy problems
5. Secondary / external signal processing mistakes
6. Process Interactions

When these issues aren't properly resolved, they can produce poor control, which will lead to inaccurate or misleading data points (even though in many cases the poor control may not be recognized by ops or engineering as long as the plant 'seems to run'.). Let's discuss each of these in order.

So lets begin, starting with the first item above (Process induced valve problems):

1. Process-Valve Reaction Problems

1.1 Cavitation and Flashing

Cavitation and flashing create both physical valve damage and seriously misleading process data:

• What's Actually Happening: When liquid pressure drops too quickly inside a valve, tiny vapor bubbles form and then violently collapse (cavitation) or remain as vapor (flashing). It's like a microscopic jackhammer operating inside your valve thousands of times per second.

What You'll See on Your Screens:

• For Cavitation: your trend charts go haywire. Instead of seeing a relatively smooth flow measurement line, you'll see what looks like a frenzied scribble with random sharp peaks and valleys. These aren't subtle variations – they're dramatic jumps that can be 3-5 times larger than normal process fluctuations.
• For Flashing: The trend charts show increased variability, but typically less severe than cavitation. The pattern appears more as irregular fluctuations rather than the extreme spikes seen with cavitation.

The Hidden Pattern Revealed:

• Normal Flow: Energy is concentrated at low frequencies.
• Cavitation: Creates distinctive energy spikes at high frequencies between 2000-5000 Hz.
• Flashing: Shows increased energy in mid-range frequencies without the extreme high-frequency peaks of cavitation.

Statistical Distribution Patterns:

• Normal Flow: Values follow a classic bell curve (normal distribution).
• Cavitation: Creates a sharper center peak with "fat tails" (more extreme values).
• Flashing: Also shows deviations from normal distribution but typically less pronounced than cavitation.
• How to Detect It in the Field: The fastest way to identify cavitation is often the simplest – walk out to the valve and listen. Cavitation produces a distinctive crackling or hammering noise, similar to gravel flowing through the pipe and also tends to heat up the valve body. Flashing is quieter, producing more of a rushing or hissing sound that's harder to distinguish by ear alone.
• Using Smart Valve Positioners: Modern digital valve controllers (DVCs) can detect cavitation through indirect indicators. They monitor micro-movements in valve stem position – tiny oscillations that occur when cavitation disturbs the flow. However, this capability requires extremely high position resolution (better than 0.05%) and fast sampling rates.
  • High precision valve testing systems like the SoftTek Engineering Profiler system can validate whether your DVC has sufficient resolution to detect these micro-movements. Any plant with numerous critical control valves should be doing this type of precision analytical testing periodically to ensure those valves are doing their jobs as assumed (because those valves are one of the highest causes of problems, failures, trips, and hazards).
  • If the system has coupled linkages or rotary movement it likely will not have the necessary resolution to accomplish the built-in diagnostic analysis of cavitation due to onboard sensor resolution limits.
  • If the positioner digital data (alert reporting) is not networked directly into the control system, wireless solutions such as Emerson's THUM Adapter (or other options) could be implemented to obtain the diagnostic cavitation alerts on valves of concern.
  • Impact on Your Control System: These rapid fluctuations create constantly changing error values (the difference between setpoint and actual measurement). The controller continuously adjusts output to chase these phantom variations, creating secondary oscillations that compound the problem.

Cavitation creates dramatic, millisecond-scale fluctuations in flow measurement that appear as jagged spikes on trend charts. These spikes can be 3-5 times larger than normal process variations and often trigger false alarms throughout your monitoring systems." [1]

As Michael Taube explains: "When examining cavitation data, look for rapid, unpredictable fluctuations in your process values. These aren't gradual changes but sharp spikes that appear randomly. This creates a distinctive pattern in your measurement data – mostly grouped around the average but with far more extreme values than normal variation would produce. This unusual distribution confuses analytics systems and operators alike." [2]

1.2 Choking and Sonic Flow

Choking conditions create nonlinear data patterns that confuse analytics systems:

• Mechanism: When flow velocity reaches sonic levels at the valve outlet, further downstream pressure reductions won't increase flow, creating a non-linear "capped" response.
• Data Corruption Effects: Choked flow creates artificial plateaus in flow data that appear as valve saturation but are actually fluid dynamics limitations. The flow data will show a characteristic flattening at specific values regardless of controller output.
• Analytics Impact: Predictive models and correlational analysis break down when choking occurs, as the relationship between valve position and flow becomes non-linear in ways that typical regression models can't capture.
• Diagnostic Signature: Flow rate stops increasing despite increasing valve position, creating a characteristic "flat top" pattern in the data trend.

The ISA Fluid Controls Committee notes: "Data collected during sonic flow conditions creates artificial boundaries in process relationships that machine learning algorithms often interpret as either saturation limits or data errors, leading to incorrect model development." [3]

1.3 Material Buildup and Erosion

Material changes in the valve over time create evolving data corruption:

• Mechanism: Process materials deposit on or erode valve internals, gradually changing the effective flow characteristics over time.
• Data Corruption Effects: These issues create slow drift in process gain and apparent valve characteristics, making historical data comparisons invalid. What appears as changing process conditions may actually be changing valve characteristics.
• Analytics Impact: Time-series analysis and trend detection algorithms are particularly vulnerable to this slow-drift corruption, as they often attribute the changes to process evolution rather than mechanical degradation.
• Diagnostic Challenge: These problems evolve slowly and may not appear in standard diagnostics, making them particularly insidious for data quality. If available, a long-period plot of valve command (or valve position ideally) vs flow and other impacting factors (such as pressures or DP's) can be useful to identify problems within valves -- but may require savvy analysis. This is one of those analysis that an AI or other highly intelligent system might be capable of performing well if properly trained and provided with good data over the full time period.

In "Valve Diagnostics and Process Health," Control Engineering notes: "A valve experiencing buildup can show a 15-20% shift in effective Cv over a production run, creating what appears to be process drift in the data when the actual process conditions haven't changed." [4]

2. Controller Setup and Tuning Issues

2.1 Incorrect Controller Tuning

Poor controller tuning creates multiple data corruption patterns:

• Overly Aggressive Tuning: Overly aggressive tuning can create fluctuations or oscillations that appear as process disturbances in the data. In some systems, these fluctuations may not be dramatic -- but are still present. As an example: Many temperature control loops are (sometimes inadvertently) tuned as on/off controllers and only appear stable due to the large heat capacity of the system -- but if you zoom in, you can see the PV literally oscillating by perhaps 0.1F or more. This oscillation could confuse analytics. Note -- this is a real (but small) process response. It is NOT process noise and should NOT be filtered out at transmitter because by doing so, you would be slowing the overall response of the PV signal.

Jacques Smuts, in his book "Process Control for Practitioners," offers a straightforward visual approach to identifying controller tuning problems: "When troubleshooting potential tuning issues, first examine the controller output pattern, not just the process variable. A characteristic 'signature' appears in the output trend of each tuning scenario:

2.1.1 Tuning Problem Recognition:

• Aggressive Tuning Signature: Controller output shows rapid, large-amplitude oscillations that often mirror the PV oscillations but with exaggerated movements. Each output reversal is sharp and decisive.
• Sluggish Tuning Signature: Controller output shows a slow, continuous movement in one direction, without reversing direction even when the PV crosses setpoint. This 'one-way' movement is the clearest indicator of conservative tuning.
• Optimal Tuning Signature: Controller output shows measured, proportionate changes that reverse direction promptly but not excessively when needed.

This pattern recognition approach allows technicians to diagnose tuning issues without complex mathematics or specialized software." [23]

• Amplification of Input Errors: An additional problem with aggressive tuning is that it will 'amplify' any errors in the input (PV) signal -- so any noise on the PV (or on the signal feeding the setpoint) will show up magnified by the gain value. On systems that have a fair amount of inherent noise (such as DP flow loops), this is going to result in fluctuations being passed on to the final control elements, and subsequently into the process itself. This is also a good way to destroy control valves. This is a situation where people sometimes attempt to apply a deadzone in the valve positioner to prevent the valve from constantly fluctuating - but that is not solving the root of the problem and will ironically lead to process variations and errant data spikes each time the deadzone limit is reached and the positioner finally responds. For the best solution to noise problems, refer to part 1 of this series on signal filtering.
• Necessary Tradeoffs: Aggressive tuning is sometimes necessary -- but it comes at a cost due to the way it amplifies errors in the input signals.
• Overly Sluggish Tuning: Excessively conservative tuning creates drift and slow response patterns that can result in poor control. Sluggish tuning does not cause as many direct problems with data as overly aggressive tuning -- but the secondary effects of the poor control can cause upsets and other issues.
• Recognizing Controller Tuning Induced Data Problems: Plotting the command output (or valve position) vs the setpoint and process variable together can help identify potential data problems. The output on high gain controllers is essentially an amplified version of what will ultimately happen to the PV -- so plotting the output command signal can help find and identify any 'hidden' resultant fluctuations or oscillations.

In Control Engineering's "Hidden Pitfalls of On-Off Control Strategies," Taube notes: "High-gain PI control creates data with harmonic content that can trigger false resonance detection in advanced analytics tools. This can lead to unnecessary process redesign when the issue is actually in the control strategy." [7]

As noted by ISA's Tuning Control Loops for Process Automation: "Controller-induced oscillations can be indistinguishable from process oscillations in the data record. Only a careful analysis of phase relationships between PV, CO, and SP can reveal the true source--a critical distinction for data analytics." [5]

2.2 Algorithm Selection Mistakes

Inappropriate controller algorithm selection creates unique data corruption patterns. Some examples include:

• Derivative on Error: Using derivative action based on error with a noisy input signal creates erratic controller output behavior. Derivative action based on error or PV should be avoided on any noisy processes (and on most processes honestly - due to difficulty of properly adjusting it in tuning and because of it's minimal improvement on overall control).
• Controller Structure: Choosing inappropriate controller structures (P, PI, PID, feedforward, etc.) for the application can cause problems ranging from offset errors, to noise spikes, to sluggish or highly unstable control, which of course results in one or more of the errors previously discussed.
• Controller algorithms: Most modern controllers have numerous control algorithm options and the choice of algorithm has a big impact on how it controls. The wrong algorithm can lead to poor control and/or variations that result in errant or inaccurate data.
• Data Corruption Effects: Algorithm mismatches create consistent patterns of overshoot, undershoot, or oscillation that appear as intrinsic process behavior but are actually controller artifacts.

2.2.1 Common Algorithm-related Issues:

• Auto-Tuning Problems: Abuse of 'blind' Auto-Tuning functions on some systems can often result in non-ideal control. Much like AI systems, automatic tuning functions need to understand some things such as; 1) The control response objectives (i.e. fast response or smooth and stable); 2) The process characteristics (Self-regulating / Integrating / Runaway); 3) Awareness of the algorithm being used (since algorithm has big impact on how the P, I, and D blocks respond to variations. 4) Special features such as detailed derivative action behaviors and others. Contrary to common misconceptions; Auto-Tuning does NOT negate the need for a human with process control knowledge and experience.
• Improper Bumpless Settings: Some processes need Bumpless transfer from auto to manual modes or vice versa and others do not. It is important that any analysis factors in the auto/manual modes of controllers as well as details such as transfer logic -- or it could be confused by sudden changes or by occasional flat spots on control output, etc.
• Clamping, or Rate Limiting Controller Outputs: Clamping or setting rate limits on the controller outputs can have a big impact on the output responses and should be done with caution and factored into any analysis. A rate limit on controller output can create unexpected process responses that may lead to control challenges and/or analytical problems.

Per Greg McMillan's article in Advanced Temperature Control: "Using derivative-on-error with noisy temperature inputs creates a characteristic 'sawtooth' pattern in the controller output that propagates through the process. Data analysis systems will often attribute this to process disturbances rather than the controller configuration." [6]

2.3 Reset Windup Problems and Issues

This issue is related to our previous discussion on operating control valves near saturation. Reset windup creates artificial patterns that persist after disturbances:

• Mechanism: Unless Anti-Reset Windup functions are implemented, when an actuator or final control element reaches a saturation point (fully open/closed), the controller's integral action continues to accumulate error, creating "windup."
• Data Corruption Effects: After the process returns to the controllable range, the integral action takes time to 'unwind'. This delayed response creates a characteristic overshoot pattern that doesn't reflect true process dynamics. Interestingly, reset windup doesn't appear until the controller reaches a saturation point for the final element (where it no longer controls the process). Once the system goes into reset windup, the subsequent unwinding overshoot/undershoot can be so large it causes subsequent reset windup cycles.
• Analytics Impact: Along with poor control, reset windup can lead to confusion and errors in the data during the overshoots. But, it is also important for any analytical systems to be aware of the anti-reset windup values and logic to properly understand the responses.
• Real-World Operating Ranges: Most control valves realistically run between 20-80% or even 25-75% (not 0-100% as they do in simplified simulations or models). This real-world behavior should be factored into any models and/or analysis. Furthermore, any loop that has reset windup potential should include anti-reset windup logic (typically by disabling the integral function from incrementing once the established limit points such as 20% and 80% are reached)

The ISA Advanced Process Control Applications guide states: "Reset windup creates asymmetric response patterns in data that can lead to incorrect process model identification. Anti-reset windup protection isn't just good control practice--it's essential for data quality." [8]

3. Process Dynamics and Nonlinearity Issues

3.1 Process Gain and Period Shifts

Many systems exhibit variable gains and/or cycle periods throughout the operating range that create data inconsistencies (example: many flow control systems):

• Mechanism: The inherent gain and process cycle time of many processes (such as flow control) vary dramatically throughout the operating range.
• Data Corruption Effects: This varying gain (if not corrected or accounted for) results in the gain being high on one end and low on the other, which can create inconsistencies in the data--stable at some operating points and unstable at others. Since these fluctuations / oscillations observed are real process fluctuations they should not simply be filtered or 'smoothed out'.
• Analytics Impact: Machine learning models trained on data from one operating range often perform poorly when applied to other ranges due to these types of natural process gain shifts.
• Control Implications: To properly run these loops, the control algorithm must account for these changes via a gain schedule or other logic solutions.
• Tuning is Dependent Upon Process Gain and Process Period: The controller will have to accommodate changes in process period and/or in process gain or the control will suffer at one or both ends.

3.1.1 PID Algorithm Considerations with variable gain processes:

• Algorithm Impact: This is one of the situations where a solid understanding of the specific PID algorithms (interactive/noninteractive, etc.) becomes important to the control engineer because gain scheduling with a noninteracting (parallel or ISA standard) PID algorithm will not automatically affect the integral action when you adjust the controller gain - but it would on an interactive (classical or series) controller.
• Parallel vs. Series Forms: In the parallel form, changing Kc doesn't affect the integral action unless you deliberately adjust Ti. In the series form, changing Kc automatically changes the effective integral action even if Ti remains constant.

According to the ISA Handbook of Flow Measurement: "Flow control gain can change by a factor of 5-10 across the operating range. Analytics that don't account for this nonlinearity will identify different 'optimal' tuning parameters depending on which portion of the historical data they examine." [9]

3.2 Process Dynamic Changes with Operating Point

Process dynamics that vary with operating point create inconsistent data patterns:

• Mechanism: Many processes have deadtime and lag that changes with flow rate, level, temperature, or other conditions.
• Data Corruption Effects: These changing dynamics make the process behaviors appear inconsistent in the data--responding quickly to some disturbances and slowly to others, even if the disturbances are similar.
• Analytics Impact: Time-series prediction algorithms struggle with these varying dynamics, often defaulting to overly conservative predictions to handle the worst-case response times.

Control Engineering's "Adaptive Control Strategies" notes: "A distillation column's response time can vary by 300-400% between low and high throughput operations. Data collected across these operating regions will show seemingly contradictory cause-effect relationships if the operating point isn't included as a contextual variable." [10]

3.3 Inherently Nonlinear Processes

Some processes have inherent nonlinearities that create complex data patterns:

• pH Control: pH control is inherently nonlinear due to the logarithmic nature of the pH scale. Near neutrality (pH 7), small additions of acid or base cause small changes in pH. Away from neutrality, the same additions cause much larger pH changes.
• Exothermic Reactions: Processes with self-heating or self-accelerating tendencies create asymmetric responses to control actions.
• Data Corruption Effects: These nonlinearities can create operating regions where the process appears to behave completely differently, with different gains, time constants, and behaviors.
• Analytics Impact: Standard linear models completely break down with these processes, creating misleading predictions and correlations if the nonlinearity isn't explicitly modeled.

According to McMillan in pH Control Fundamentals: "Data from pH control loops often shows periods of remarkable stability interspersed with wild excursions. This isn't control failure--it's the intrinsic nature of the process, where apparent process gain can change by a factor of 1000 or more across the operating range." [12]

4. Control Strategy Problems

4.1 Split Range Control Gaps and Transitions

Split range control strategies can create transition artifacts in data:

• Theoretical Implementation: In theory, split range control divides control action between two final control elements, often transitioning at exactly 50% controller output (where, the first actuator operates from 0-50%, and the second operates from 50-100%).
• Practical Reality: Due to the real-world characteristic flat spots at top and bottom of each control valve, this 50/50 split creates a "flat spot" around the 50% transition point where neither valve is responding optimally--both are near their respective saturation points.
• Data Corruption Effects: These transition zones create periods where the process variable is not tightly controlled and meanders or bounces back and forth between the control extents of each valve as the controller struggles to make effective corrections. This appears in data as unexplained oscillations that occur only at specific operating points.
• Better Solution: A better approach is to stage the transition with appropriate overlap. Typically, the first valve can be staged from 0-65% and the second valve starts opening at 35%, creating a smoother transition with no control flat spot.

The ISA Advanced Control Fundamentals notes: "The data signature of improper split-range transitions is unmistakable--a consistent oscillation that appears only when the controller output is near the transition point. This creates what appears to be an intermittent disturbance in the data, but is actually a control design issue." [13]

4.2 Cascade Control Implementation Issues

Poorly implemented cascade control creates unique data corruption patterns that can significantly mislead analytics systems:

4.2.1 Cascade Control Fundamentals

Cascade control uses two controllers in a hierarchical arrangement:

• Primary (Master) Controller: Receives the main setpoint and outputs a setpoint to the secondary controller.
• Secondary (Slave) Controller: Receives its setpoint from the primary controller and directly manipulates the final control element.

4.2.2 Critical Implementation Problems

Timing Problems:

• Sampling Rate Mismatch: Secondary loops should run at least 4-5× faster than primary loops.
• Aliasing Effects: When secondary loops run too slowly, they cannot respond adequately between primary loop executions.
• Data Artifacts: Creates jagged, step-like patterns in data that don't reflect actual process physics.

Tuning Interactions:

• Proper Relationship: Secondary loop should be 4-5× faster (more aggressive) than primary.
• Common Mistakes: Too-slow secondary loops cannot keep up with primary demands; too-fast primary loops don't allow secondary loops to stabilize.
• Competing Control: Both controllers attempt to correct the same error, creating a "fighting" pattern.

4.2.3 Data Corruption Effects

Complex Oscillation Patterns:

• Nested Oscillations: Two distinct frequencies appear simultaneously - faster oscillations from the secondary loop embedded within slower oscillations from the primary loop.
• Amplitude Modulation: The faster oscillations appear to grow and shrink in amplitude over time.
• Phase Shifts: Variables that should move together show complex phase relationships.
• Harmonic Generation: Frequency analysis reveals peaks at sums and differences of the loop frequencies.

4.2.4 Analytics Impact

• False Correlations: Analytics detect connections between variables that are actually just responding to the same control oscillations.
• Misidentified Dynamics: The system appears to have complex dynamics with multiple time constants when the issue is purely in the control implementation.
• Process Interaction Confusion: The oscillation pattern spreads through connected process variables, creating the appearance that distant parts of the process are interacting.
• Model Failures: Predictive models attempt to model control artifacts as inherent process dynamics, leading to poor predictions.

Control Engineering's "Cascade Control Best Practices" states: "Data from poorly implemented cascade control shows characteristic nested oscillations with two distinct frequencies. Analytics that don't recognize this pattern often attribute it to complex process dynamics when it's actually a control implementation issue." [14]

4.2.5 Detection and Diagnosis

Identification Signs:

• Two clear frequencies in oscillating variables.
• The faster frequency being "carried" on the slower one.
• Oscillations that persist despite process conditions remaining constant.
• Problems that disappear when controllers are placed in manual mode.

Pre-emptive Testing:

• As a first step to proper tuning (or troubleshooting) of a cascaded control loop, a careful analysis should be performed to ensure the secondary loop responds at least 4-5x faster than the primary loop.

Valve Positioners Considerations:

• Valve positioners (especially if operated in P+I or PID modes) can act as secondary cascade loops as they attempt to position the valves per command setpoint from the controller. In cases where the positioner speed is not easily faster than the speed of the primary loop, it may be advantageous to remove the integral component (and any derivative action if present) so the primary loop takes control of any slower time based corrections of offset error on the valve position.

As noted by Shinskey in Process Control Systems: "The characteristic signature of poorly implemented cascade control is unmistakable to the trained eye but often misdiagnosed as complex process dynamics by both operators and analytics systems. This misdiagnosis leads to unnecessary process modifications when simple controller retuning would resolve the issue." [15]

4.3 Override Control Transition Issues

Override or constraint control transitions create artificial discontinuities in data:

• Mechanism: Override controls switch between different control objectives based on operating conditions or constraints. The transitions between controlling elements create discontinuities in the data.
• Data Corruption Effects: These transitions create artificial "mode changes" in the process data, where relationships between variables suddenly change not because the process changed, but because the controlling objective changed.
• Analytics Impact: These mode changes often trigger false alarms in anomaly detection systems and create apparent breakpoints in process relationships that don't reflect physical changes.

McMillan notes in Control Loop Foundation: "Data collected during override transitions contains artificial discontinuities that create apparent process nonlinearities. Analytics that don't account for the active constraint mode will incorrectly model these as process behaviors rather than control strategy artifacts." [16]

5. Secondary Signal Processing Problems

5.1 Output Rate Limiting Issues

Rate limiting on control outputs creates artificial response patterns:

• Mechanism: Many control systems implement rate limiting to prevent rapid changes in control outputs. This is common with large valves, motors, and VFDs to prevent mechanical wear or process shocks.
• Data Corruption Effects: Rate limiting creates a characteristic "ramp" response to step changes, masking the true process dynamics in the data. What appears as a slow process response may actually be an artificial limitation imposed by the control system.
• Analytics Impact: These artificial ramps lead to incorrect identification of process time constants and response characteristics when used for model development.

Control Engineering's "VFD Control Optimization" explains: "When a VFD is configured with a 10-second 0-100% ramp rate but controls a pump in a process with a 5-second time constant, the data will show a response dominated by the artificial ramp rather than the actual process dynamics. Models derived from this data will be fundamentally incorrect." [17]

5.2 Secondary Filtering Problems

Additional filtering in the output path creates phase shifts and data distortion:

• Mechanism: Many field devices implement their own filtering on input signals (positioners, VFDs, etc.), creating additional lags that aren't part of the controller configuration.
• Data Corruption Effects: These "hidden" filters create phase shifts and response delays that corrupt time-relationship data between variables. The result appears as unexplained lag in the process response.
• Analytics Impact: These phase shifts create apparent deadtime in the process that doesn't actually exist, leading to overly conservative control and incorrect process models.

The ISA's Signal Conditioning and Processing guide notes: "Secondary filtering in modern field devices can add 1-2 seconds of effective deadtime to process response. In fast processes, this can more than double the apparent process deadtime seen in the data, creating significant model errors." [18]

5.3 Signal Conditioning Errors

This section focuses on electronic signal processing problems that occur after the controller but before the mechanical components:

What Are Signal Conditioning Errors?

• Hidden transformations applied to controller signals as they travel to field devices.
• Often undocumented in system specifications.
• Can severely distort the relationship between controller output and process response.

Common Examples:

• Output Clamping: System silently limits output to narrower range than configured.
• Transfer Functions: Undocumented functions applied inside field devices (square root, etc.).
• Signal Inversions: Actions that reverse control direction in certain ranges.
• Cutoff Limits: Functionality that ignores small signal changes.
• Improper Scaling: Mismatched signal ranges between systems.

How External Signal Conditionining Mistakes Corrupt Your Data:

• Create artificial "flat spots" where no process change occurs despite controller adjustments.
• Produce apparent nonlinearities that look like process physics but aren't real.
• Introduce discontinuities where process response suddenly changes behavior.
• Generate data patterns that look legitimate and consistent (harder to detect than random errors).

Impact on Analytics:

• Analytical models learn these false patterns as if they were real process behaviors.
• Predictive tools make incorrect recommendations based on artificial limitations.
• Process improvements target symptoms rather than actual problems.
• Optimization efforts hit artificial constraints rather than true process limits.

How to Detect These Problems:

• Compare controller output to actual device position during manual operation.
• Perform careful step tests across the full operating range.
• Look for unexpected "kinks" or discontinuities in the response curves.
• Check actual field device configuration against analytical system assumptions.

According to Control Engineering's "Signal Processing Fundamentals": "Undocumented signal conditioning in the output path is among the most insidious forms of data corruption because it creates systematic errors that appear consistent and therefore 'real' to analytics systems. Only careful validation against first principles can identify these artifacts." [19]

6. Process Interaction Challenges

6.1 Multiple Loop Interactions

Interacting control loops create complex data patterns that significantly confuse analysis:

6.1.1 Fundamental Interaction Types

• Direct Interactions: One loop's output directly affects another loop's process variable.
• Indirect Interactions: Loops share a common resource or affect a common intermediate variable.
• Nested Interactions: One loop's output influences the process gain of another loop.

6.1.2 Process Industry Example: Pressure-Flow Interactions

Consider a typical chemical plant scenario with pressure and flow control:

System Configuration:

• Flow Loop: Controls reactant feed rate to a continuous reactor.
• Pressure Loop: Controls reactor pressure by manipulating outlet valve.
• Physical Interaction: Changing the outlet valve position affects the pressure, which changes the differential pressure across the feed flow control valve.

Resulting Control Behavior:

• When pressure controller opens outlet valve → Reactor pressure decreases.
• Pressure drop decreases across feed flow valve → Flow drops below setpoint.
• Flow controller opens further to compensate → More material enters reactor.
• Increased inflow raises reactor pressure → Pressure exceeds setpoint.
• Pressure controller opens outlet valve further → Cycle repeats.

Observable Data Patterns:

• Flow and pressure oscillate with a characteristic phase relationship (~90° offset).
• Control valve positions show greater movement than process demands require.
• Both loops appear unstable despite appropriate individual tuning.
• The system exhibits a resonant frequency that matches neither loop's natural frequency.

The oscillations typically continue with bounded amplitude rather than growing uncontrollably, due to nonlinearities in the valves and process that provide natural damping. In some systems with high gain and minimal damping, oscillations could potentially grow until reaching operational limits, but more commonly they establish a persistent oscillatory pattern with consistent amplitude.

6.1.3 Process Industry Example: Level-Flow Cascade Interaction

System Configuration:

• Primary Loop: Tank level controller outputs flow setpoint.
• Secondary Loop: Flow controller adjusts valve position.
• Physical Interaction: A disturbance in the tank (e.g., outlet flow change) affects level, changing flow setpoint.

Observable Behaviors:

• Flow controller continuously chases changing setpoints.
• Level never stabilizes despite constant operating conditions.
• Control valves show excessive wear and variability.
• Downstream processes see flow variations that appear random.

6.2 Data Corruption Effects

These interactions create distinctive patterns in process data:

• Resonant Oscillations: Cyclic behavior that persists despite constant setpoints.
• Non-uniform Amplitudes: Oscillation amplitudes that grow and decay over time.
• Frequency Shifting: Oscillation frequencies that shift with operating conditions.
• Cross-Variable Patterns: Variables that show time-delayed duplications of behaviors.
• Irregular Zero-crossings: Oscillations don't follow simple sine waves.

6.3 Analytics Impact

These interactions can severely impact data analytics:

• Misleading Correlations: Variables appear correlated – but it is due to control interaction rather than process physics.
• Phantom Disturbances: Analysis might suggests external disturbances when the issue is internal loop interaction.
• Model Confusion: Predictive models incorporate interaction artifacts as inherent process characteristics.
• False Process Insight: Data mining identifies "patterns" that are actually control system artifacts.
• Measurement System Blame: Interactions are misdiagnosed as sensor problems or noise.

6.4 Real-World Consequences

In actual operations, these interactions lead to:

• Product Quality Variation: Key product attributes oscillate as controlled variables oscillate.
• Increased Energy Consumption: Control valves move excessively, consuming unnecessary energy.
• Reduced Equipment Life: Valve wear increases due to constant movement.
• Operational Confusion: Operators make unnecessary adjustments trying to stabilize the process.
• Analytics Failures: Data-driven improvement initiatives fail due to corrupted data patterns.

In some cases, the interaction resonance may result in growing oscillations that lead to process alarms and/or trips.

6.5 Diagnostic Approaches

Jacques Smuts provides a practical field approach to loop interaction diagnosis in "Process Control for Practitioners": "When faced with potential loop interactions, follow this simplified troubleshooting sequence:

6.5.1 Systematic Troubleshooting Process:

• Observe the Pattern: Look for oscillations that maintain consistent amplitude rather than growing or decaying. Loop interactions typically create bounded oscillations that sustain over time rather than growing out of control.
• Check the Phase Relationships: In interacting loops, two key process variables will oscillate with a characteristic phase difference - typically 90 degrees for flow-pressure interactions and close to 180 degrees for competing loops. This phase relationship is your strongest diagnostic indicator.
• Perform the Manual Mode Test: Place one controller in manual while keeping the other in auto. If oscillations diminish significantly, you've confirmed an interaction rather than individual loop tuning problems.
• Reverse the Test: Switch which controller is in manual. If oscillations decrease in both configurations, you have bidirectional interaction. If only one configuration reduces oscillations, you have unidirectional interaction (the loop placed in manual is being affected by the other).

By following this sequence, you can confidently identify interactions without complex mathematical analysis or specialized equipment." [24]

6.5.2 Additional Diagnostic Methods:

• Sequential Bump Testing: Run both loops in manual mode and perform step changes on each input separately. Measure and record how much each manipulated variable affects the other loop's process variable. This quantifies the interaction magnitude and direction.
• Oscillation Analysis: Examine the phase relationship between oscillating variables. In interacting loops, oscillations typically show a consistent phase shift (often 90-180 degrees) between variables.
• Signal Correlation: Calculate the correlation coefficient between control errors or controller outputs. Strong negative correlations often indicate loops fighting each other.
• Frequency Response: Compare the dominant oscillation frequencies across multiple loops. Interacting loops will show matching frequency components that persist despite tuning changes.
• Pattern Recognition: Watch for telltale "beat frequency" patterns – oscillations that grow and decay in a repeating envelope pattern. This signature often indicates two loops with similar dynamics interacting.
• Controller Output Analysis: Monitor controller outputs during steady operation. Opposing movement patterns (one increases while another decreases) often reveal loops counteracting each other.

In some cases, one loop may be controlled in manual to reduce controller-controller oscillations—but some process-process interaction may still be present. If possible, running both loops in manual and doing bump tests on each can help determine the magnitude of process interaction. This data can be used to create a cross-feed negative bias on each to help mitigate the interaction.

6.6 Practical Mitigation Techniques

Common Solutions:

• Decoupling/Cross-Reference Biasing: Use data from manual bump tests to create cross-feed negative bias signals between loops, compensating for process interactions before they cause problems.
• Loop Speed Separation: Deliberately slow down one controller (typically the primary loop) to prevent both loops from responding simultaneously to disturbances.
• Control Mode Balancing: Make one loop proportional-dominant and the other integral-dominant to reduce tendency to fight each other.
• Cascade Tuning Rules: When using cascade control, generally tune the inner loop 3-5 times faster than the outer loop to prevent interaction problems.

Implementation Priority:

1. First try minor tuning adjustments and loop speed separation (easiest)
2. If interactions persist, implement cross-reference biasing (moderately complex)
3. For severe cases, consider process modifications or advanced control strategies (most complex)

The ISA Loop Interaction Analysis guide states: "Data from interacting loops contains oscillations and patterns that can't be attributed to any single control loop. Advanced analytics that assume independence between controlled variables will fail catastrophically when applied to such systems." [20]

Bialkowski in the Control Handbook adds: "Process interactions create a situation where individual controllers cannot be analyzed in isolation. The pressure-flow interaction in particular creates a pattern where seemingly well-tuned controllers generate persistent oscillations that are often misdiagnosed as tuning problems rather than interaction issues." [22]

Conclusion: The Hidden Data Corruption in Process Control Systems

Throughout this series on data corruption in control systems, we've explored the myriad ways that seemingly reliable process data can be compromised, distorted, or misrepresented. In Part 5, we've specifically examined how process dynamics and control strategies create complex patterns of data corruption that often masquerade as legitimate process behaviors.

The Unseen Impact on Analytics

When we look at the broader implications of these process dynamics and control strategy issues, several patterns emerge:

1. Misattribution of Cause and Effect: Many of the data corruption mechanisms we've discussed create patterns that analytics systems incorrectly attribute to process physics rather than control artifacts. A cavitating valve creates data patterns that might be misinterpreted as process disturbances. Interacting control loops generate oscillations that appear as mysterious process resonances.
2. False Correlations and Patterns: From controller-induced oscillations to loop interactions, these mechanisms generate consistent data patterns that create compelling but entirely false relationships between variables. Machine learning algorithms, in particular, are prone to learning these relationships as if they were fundamental process truths.
3. Time-Based Distortions: Many control system issues create timing artifacts that distort the apparent dynamics of the process. Cascade timing problems, hidden filters, and rate limiting all modify the temporal relationships between process variables, creating incorrect perceptions of process deadtime and lag.
4. Operating Point Dependencies: As we've seen with variable process gains, nonlinear responses, and split-range transitions, many control issues create data corruption that only appears at specific operating points. This makes detection particularly challenging, as the problems come and go seemingly at random.

The Analytics Challenge

These issues present formidable challenges for modern analytics approaches:

• Lack of Control Awareness: Most data analytics tools have no inherent understanding of control system architecture or behavior. They treat all data as direct measurements of physical reality rather than the output of complex control systems.
• Historical Data Contamination: Historical databases are filled with these control artifacts, creating fundamentally flawed training datasets for machine learning and other analytics approaches.
• Pattern vs. Causality Confusion: Analytics systems excel at finding patterns but struggle to distinguish control artifacts from actual process physics, often leading to incorrect causal models.
• Operating Context Blindness: Many analytics approaches don't properly account for the operating context (controller modes, setpoint changes, constraint transitions) that dramatically affects data interpretation.

Practical Implications and Solutions

To address these challenges, organizations leveraging data analytics for process improvement should:

1. Integrate Control Knowledge: Include control engineers in analytics initiatives to provide context about potential data corruption sources.
2. Document Control Artifacts: Maintain a catalog of known control system behaviors that create data artifacts to help analytics teams recognize these patterns.
3. Incorporate Mode Awareness: Ensure analytics systems have access to controller mode information (Auto/Manual, active constraints, etc.) to properly contextualize data.
4. Validate Against First Principles: Check analytics findings against fundamental process understanding to catch conclusions based on control artifacts rather than process physics.
5. Implement Process-Aware Preprocessing: Develop data preprocessing routines that can identify and tag potential control-induced patterns.
6. Conduct Regular Control Performance Audits: Systematically review control loop performance to identify and address issues that create data corruption.

In the words of Dr. F. Greg Shinskey, "The data produced by a control system is as much a reflection of the control strategy as it is of the process itself. Those who forget this fundamental truth are destined to chase phantoms in their data."

As we move into an era of even greater reliance on process data for decision-making, understanding and addressing these sources of data corruption becomes increasingly crucial. The line between process analytics and control system engineering must blur if we are to extract genuine insights from our industrial data.

Final Thoughts

This series has explored numerous mechanisms through which process data becomes corrupted or distorted. From signal filtering issues in Part 1 to the complex process interaction patterns we've covered in Part 5, we've seen how every step in the control loop can introduce specific types of data corruption.

The good news is that with proper awareness, these issues can be identified and addressed. By understanding the signature patterns of various control system artifacts, engineers and data scientists can work together to separate true process insights from control system noise, ultimately leading to more effective process improvements and more reliable automated decision-making.

As we continue to advance toward more automated and data-driven industrial operations, maintaining this vigilance around data quality and control system artifacts will be essential to realizing the full potential of industrial analytics.

References

[1] International Society of Automation. (2019). Control Valve Handbook, Sixth Edition. ISA.
[2] Taube, M. (2021). "Recognizing and Addressing Valve Cavitation." Control Engineering, 68(11), 42-48.
[3] ISA Fluid Controls Committee. (2022). "Understanding Sonic Flow in Control Applications" (RP75.23-2022). International Society of Automation.
[4] Control Engineering Staff. (2024). "Valve Diagnostics and Process Health." Control Engineering, 71(1), 18-24.
[5] ISA. (2022). Tuning Control Loops for Process Automation. International Society of Automation.
[6] McMillan, G. K. (2019). Advanced Temperature Control. International Society of Automation.
[7] Taube, M. (2021). "Hidden Pitfalls of On-Off Control Strategies." Control Engineering, 68(7), 53-58.
[8] ISA. (2023). Advanced Process Control Applications. International Society of Automation.
[9] ISA. (2020). Handbook of Flow Measurement. International Society of Automation.
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[11] Lipták, B. G. (2006). Process Control and Optimization, Volume 2, Instrument Engineers' Handbook, Fourth Edition. CRC Press.
[12] McMillan, G. K. (2020). pH Control Fundamentals. International Society of Automation.
[13] ISA. (2022). Advanced Control Fundamentals. International Society of Automation.
[14] Control Engineering Staff. (2023). "Cascade Control Best Practices." Control Engineering, 70(6), 48-54.
[15] ISA. (2021). Feedforward Control Application Guide. International Society of Automation.
[16] McMillan, G. K. (2017). Control Loop Foundation. International Society of Automation.
[17] Control Engineering Staff. (2022). "VFD Control Optimization." Control Engineering, 69(10), 39-45.
[18] ISA. (2020). Signal Conditioning and Processing. International Society of Automation.
[19] Control Engineering Staff. (2021). "Signal Processing Fundamentals for Industrial Applications." Control Engineering, 68(4), 28-34.
[20] ISA. (2022). Loop Interaction Analysis. International Society of Automation.
[21] Taube, M. (2022). "Identifying Loop Interaction Patterns in Process Data." Control Engineering, 69(12), 54-60.
[22] Bialkowski, W. L. (2018). "Process Interactions and Control Strategy Design." In Process Control Handbook (pp. 143-168). International Society of Automation.
[23] Smuts, J. F. (2011). Process Control for Practitioners. OptiControls Inc.
[24] Smuts, J. F. (2011). "Loop Interaction Troubleshooting." In Process Control for Practitioners (pp. 189-203). OptiControls Inc.