Remaining Useful Life (RUL) Predictive Models for Industry 4.0
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Predicting Remaining Useful Life (RUL) is one of the core benefits of the Industry 4.0 approach. Due to the rapid deployment of Internet of Things (IoT) devices, data sources for variables such as vibration, pressures, current, and temperatures are now becoming widely and readily available. This — coupled with digital maintenance records —  provides insight into equipment health like never before.
The access to this data could not have come at a more opportune time. Concurrent with the explosion of new digital equipment comes two other important developments: an aging workforce and deep learning advances. In the past, predicting when equipment should be replaced relied heavily on subject matter expert input. This expertise was dependent on select individuals with highly specialized knowledge. As the workforce in the U.S. continues to age, many of these individuals are leaving the industry and creating a significant knowledge gap. Also, the advent of cheap GPUs and significantly deeper models has increased the potential for high-quality artificial intelligence options for predicting failures before it happens. Before we jump into the methodology, let's take a look at what Remaining Useful Life (RUL) is.
'To fully realize the benefits of Industry 4.0, Remaining Useful Life (RUL) predictive models must be developed and implemented.' -Wesley O'Quinn
RUL is the length of time a machine is likely to operate before it requires repair or replacement. By taking RUL into account, engineers can schedule maintenance, optimize operating efficiency, and avoid unplanned downtime. Although nuanced, this concept is different when compared to anomaly detection. Anomaly detection could play a part in predicting remaining useful life, but it is more focused on emergent events which will rapidly degrade equipment performance. RUL focuses more on long-term asset management and is measured in terms of years rather than days.
The methods used to predict RUL are significantly varied, but can be roughly sub-divided into three basic categories:
The supervised machine learning method is likely the most intuitive, but it tends to be the most costly from a data annotation perspective. To truly be able to train a totally supervised model, one needs a massive amount of data with multiple, full-lifetime runs to be effective. Additionally, although anomaly detection and RUL are two different things, the ability to provide anomaly detection inputs into an RUL model is important. By the very definition of anomaly detection, the data is simply that, anomalous. This means even with a large cache of data, certain scenarios may not be present. To help visualize this concept, see figure 1 below.
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