Your Asset Reliability Program is Only as Reliable as Your Sensor
by Chris Kramm , Wilcoxon Sensing Technologies
Over the last few years , we ’ ve seen an increased emphasis on digital transformation by industrial operators . Large operational and machine health datasets have become a focal point to feed our optimization activities . Data scientists realized early on that machine learning analytics techniques were only as effective as the datasets from which they were trained . Good data drove convergence and clarity ; bad data drove uncertainty and ambiguity . Good data was important .
Our focus on data was not without fault . We initially looked to big data insights – the results of our cutting-edge analytic techniques – as the primary contributor of value . We started to speak about control systems , SCADA and machine condition monitoring systems as data sources . We tried to convince ourselves we could ‘ clean up ’ the data in software . Our efforts fell short …
As it turns out , accurate machine health insights rely heavily on hardware , beginning with the sensor . Sensors perform a critical role in the creation of insights : they measure physical properties of a machine or process and convert these properties into signals that can be acquired by sophisticated measurement systems . Once filtered to reduce noise and further post-processed into data , they help create direct relationships to machine or process health .
Don ’ t be fooled by low-cost sensing options , which often sacrifice difficult-to-verify sensor characteristics , such as design reliability and measurement stability , in favor of cost-saving design and manufacturing techniques . The loss of value due to poor data quality quickly eclipses any savings of a low purchase price .
• Low-performance sensors often experience stronger effects from electrical interference emitted from machines or other electronic devices . This interference can manifest as an ‘ apparent ’ machine fault in the signal – and can be misinterpreted by typical software and analytics techniques .
• Low-quality sensors may experience measurement drift due to changes in the environment ( such as changes in temperature ) or the natural ageing of the sensors itself . These changes can make a mess of datasets , since they track the behavior of the sensor or environment , not the behavior of the machine .
• Low-cost sensors with short lifetimes can present as a nuisance in the plant . Even when the replacement sensor is warrantied , maintenance or instrumentation personnel have the time-consuming task of re-installing the sensor . Worse , faulty sensors often go unaddressed for months as higher-priority repairs or maintenance activities take precedence . During that time , vibration practitioners and analytic software are ‘ blind ’ to developing machine issues .
The true value of a sensor comes from the optimization or cost avoidance it delivers , like avoiding down-time and early fault detection , so that developing problems can be corrected at the lowest cost . Further value comes from not having to replace sensors during the life of a machine and through the creation of data not influenced by factors unrelated to the health of the machine .
The importance of data to digital transformation activities is inextricably linked to sensors delivering good data . It is my hope you will consider the entire signal chain , from sensor to monitoring system to analytics to insights to actions to outcomes . Every step in the chain is critical . Every step in the chain contributes to data integrity – or reduces it . A bad sensor compromises the effectiveness of the remaining steps in the measurement chain , invalidates insights , and leads to incorrect outcomes . For this reason , it is important to specify high-quality sensors and ensure physical measurements are accurately and reliability converted into the signals and data necessary for correct insights and successful outcomes .
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