Bearing failure diagnosis and prognostics modeling in plants for industrial purpose

Abstract

Abstract When condition-based maintenance (CBM) is combined with proper decision support systems, it leads to enhanced utilization of resources and increased productivity which tends towards business efficiency. The forecasting of the future condition, the remaining operating life, or probability of stable system behavior, based on data from acquired condition monitoring is referred to as prognosis which is an important part of the CBM process. Despite auto-regression integrated moving average (ARIMA) time series modeling, being long established and dating back to the 1960s, it has surged through new advances over the years and is now recognized as a major forecasting technique. Its application is therefore investigated here in the context of the FEMTO–ST Institute (Franche-Comté Électronique Mécanique Thermique et Optique-Sciences et Technologies) bearing dataset. The work discussed in this article uses a time series approach which contributes to modeling and forecasting the remaining useful life (RUL) of bearings in plants, thereby helping to prevent catastrophic failure before it occurs. The motivation for this paper lies in the approach used in structuring the ARIMA models, thereby adding value in its application by first ensuring the stationarity of the time series signal by using the Dickey-Fuller Test, which then makes forecasting easy and accurate. The result obtained here using ARIMA is compared to the results obtained in the literature where neural network regression (NNR) was used as part of the FEMTO competition. We checked by contrasting our observations with the NNR observations obtained as well as the experimental results from the National Aeronautics and Space Administration (NASA)

Keywords

• ARIMA model
• Artificial intelligence (AI)
• Condition-based maintenance (CBM)
• Dickey-Fuller Test
• Forecasting
• Naive model