Processing, June 2020

JUNE 2020 www processingmagazine com 15 Direct abnormality detection of rotating equipment is often made using vibration and power consumption data but simple observation of these variables is not sufficient to predict problems A more comprehensive approach uses machine learning to detect changes among various process variables during abnormal operations as compared to periods of normal operation to predict failures in the early stages The upper part of Figure 1 shows the change of process variables over time with abnormality occurring at the right side of the upper graph The lower graph shows the output values of a machine learning model created using multiple process variables as inputs for the purpose of early detection of abnormality The output value of the machine learning model rises much earlier than the raw process data in the upper graph would suggest allowing early abnormality detection Root cause analysis Performance degradation due to contamination of compressors leakage due to corrosion of piping and tanks clogging of equipment due to polymerization and other issues can often be detected early through root cause analysis This is typically done by analyzing differences between normal operations and periods when failure has occurred referred to as abnormal conditions To identify the root cause a hypothesis is made and then verified using process data When abnormal condition data can be collected in sufficient quantities machine learning algorithms can be applied with results showing which process variable had a high degree of contribution Figure 2 Approach 1 However an issue often arises because abnormal condition data is limited because plants of course try to avoid these states of operation In this case operational states can be divided into several groups using a technique called clustering with the group including the abnormal operation state examined in detail Based on the characteristics of this group the condition in which the possibility of abnormality is high can be derived Figure 2 Approach 2 There is another machine learning technique that can be used to determine what cluster is abnormal by learning the features associated with normal data and applying these learnings to a data set When a cluster is determined to be abnormal the process variables with high contribution can be identified and ranked Figure 1 Machine learning algorithms can be used to uncover anomalies and predict problems Figures courtesy of Yokogawa Figure 2 Analysis of factors causing deterioration due to abnormal conditions These technologies are particularly effective when used to automatically discover problems by examining the vast amounts of data collected and stored in a typical process plant