Statistical Process Control (SPC) has long been applied in discrete manufacturing operations as a predictive diagnostic technology. Statistical Process Monitoring (SPM), the continuous process version of SPC, has been applied less often as highlighted by Emerson’s Tom Wallace and John Miller in an Intech magazine article, Statistical process monitoring turns process noise into valuable information.
The authors note what types of abnormal process conditions can be detected through SPM:
…plugged impulse lines, loss of agitation, entrained air, process leakage, cavitation, and column flooding—generates a specific signature, identifiable by a close analysis of process noise, standard deviation, coefficient of variation, or both. These, along with the mean, vary considerably from process to process, and SPM cannot identify the specific cause of an abnormal situation without the participation of the user, but they provide the data needed to make predictions.
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Tom and John explain that:
Much of the mathematic calculations required to create the variables used in SPM are best done in the transmitter, as many of today’s smart transmitters can calculate the individual SPM variables and calculate the appropriate adaptive limits and alert values. That information forms the first part of the SPM.
The next part is what is displayed back to operators and process engineers in the control system:
It uses the SPM data generated by the smart field device to create a process fingerprint. It also provides a data historian with time-synchronized alarms, handles alarm management, and correlates multiple process loops for multi-loop SPM and process optimization.
Tom and John note the difficulty in manually implementing an SPM solution on your own. In an earlier post, Simplifying Access to Abnormal Condition Detection Diagnostics, I described how a human centered design (HCD) approach was taken to simplify this implementation for process manufacturers with DeltaV control systems and Rosemount measurement devices. A whitepaper, DeltaV pre-engineered control module templates and operator faceplates to facilitate development and use of SPM diagnostics originating in Rosemount pressure instruments describes the details of this implementation.
The authors share great advice on how to get started incorporating SPM diagnostics into your process. It:
…works by using process information to generate signatures, the first step in implementing the automated system is to consult with the operators and tap their insight and intuition to use the knowledge they possess as part of the implementation process.
Next:
…make an educated guess as to the problems that are likely to arise in the plant and the points at which these problems can be detected—pumps that are likely to cavitate, agitators that tend to stop, columns that are subject to flooding, lines that tend to plug, and so on.
Next, establish:
…trend lines of what normal operation looks like; establish a baseline when the process is running at steady state. It is best at this point to disable any SPM-based alarms until having developed a good understanding of normal operation.
Following that step:
…set the alarm limits and run with alarms enabled. In some systems, the field devices have the ability to automatically and adaptively calculate those limits.
When abnormal conditions occur, capture all the related data, analyze it, and adjust accordingly.
They advise to collect this information into an operator reference book making sure to document normal conditions (process signatures.) The abnormal conditions should then be captured along with the corrective actions taken to restore the process to normal operations. This includes capturing:
…the process signature of upstream, downstream, or any related parameters that could have a cause/effect relationship. Then examine the records to identify and document the earliest reproducible signature of each abnormal condition. This may involve data from the monitored point or from upstream or related points.
An incremental approach should be taken to implement SPM, building on success. The authors conclude:
As experience is gained in implementation in one part of the plant, other parts should go along more easily, with less time needed for learning, design, and configuration. With experience, engineering will gain additional process engineering insight and learn to diagnose and eliminate root causes of process problems. Operators will gain additional insight into the processes they control, learn to anticipate and prevent abnormal situations, be able to reduce process upsets, and be able to operate the process closer to the limits.
The article provides great “how to” guidance for incorporating SPM predictive diagnostics in your process.
