Advanced Condition Monitoring Practices for Improving Plant Reliability

Abstract

 The paper’s objectives are to examine the application of state-of-the-art statistical methods and artificial intelligence in monitoring of vital assets to offer timely intervention in case of failures and enhance maintenance procedures. Moreover, this research also intends to assess the effectiveness of Fast Fourier Transform and Wavelet Transform tools in determining the health condition of rotating machinery and also in diagnosing early mechanical faults. Following data preprocessing, a model is developed which can then be used for the prognosis of subsequent failures and their assessments pertaining to your maintenance planning. In “Prediction” cluster, predictions to future failure based on outputs attained from the set model are made. ‘Visualization’ is one of the clusters that show how maintenance distribution can be represented besides showing how one can forecast the next failure. As shown in this research, it provides a structure to the planning and scheduling of maintenance work orders by the generation and display of graphical representations derived from sensor data, thereby improving outcomes of operational reliability and cost in industrialized environments. This research provides a comprehensive analysis of the use of predictive maintenance practices of a manufacturing environment. This paper discusses the use of these methods in combination with the real-time monitoring of sensors and methods of detecting anomalies. This paper discusses the ability of machine learning algorithm like the Random Forest classifiers for supporting the prediction of equipment failures from the sensors data. Therefore, this research will give more insight into the use of predictive maintenance systems and how they can be used to maximize the performance of the equipment and also increase the useful life of the assets.