Performance Monitoring and Failure Prediction of Industrial Equipments using Artificial Intelligence and Machine Learning Methods: A Survey

Abstract

Performance monitoring and failure prediction of industrial equipment plays a very important role not only in the quality of the manufactured material but also in the amount of time and money saved in the overall maintenance. This paper seeks to survey the general research development and advancement in the use of AI/ML techniques for equipment fault prediction in industries over time. The topics surveyed in this paper include various algorithms, use cases and concepts that pertain to the use of such technology in a wide range of industries including oil and gas, coal, automotive industry, etc. This survey addresses early research work done between the late 80s to the early 2000s, the recent research done between the early 2000s to 2017 and the latest research, the work done in the past two years. It can be concluded that this paper makes a thorough survey of different ML/AI methods used in the Industrial Manufacturing domain. Methods like LSTM, Bi-LSTM, ANNs and SVM classifiers were found to be some of the popular approaches used.