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Top1. Introduction
In this era of technology, ML is playing a vital role in making our life easier. Basically ML is the scientific study of the statistical model that computer system uses to perform specific task without any explicit instruction. It relies on patterns and inference instead. Automobile sector is one such sector which is using ML. In the automobile sector the automobile maintenance has been the major talk in today’s scenario. All machines are prone to failure. Automobile maintenance can be carried out by three major maintenance policies (Susto et al., 2012): Run to failure R2F, PvM and PdM. In R2F, the actions are carried out after the failure thus this type of maintenance is least effective as the cost associated is more and downtime gets increased. In PvM, the actions are carried out according to planned schedule. The problem with PvM is that it involves an unnecessary action which leads to the wastage of the resource and increase in cost. In PdM, the actions are carried out based on an estimated health status of a piece of equipment. PdM systems allow advance detection of pending failures. (Krishnamurthy et al., 2005)
In automobile maintenance and repair, it is necessary to properly diagnose the cause of failure (Wang et al., 2018) so that one does not needlessly replace or worsen vehicle components. To be able to gather these information PdM can serve as the effective solution (Mobley, 2002).We can use ML for the purpose of PdM in the automobiles. This can help us in reducing associated cost and downtime thus increases the efficiency.
In this paper, we take into consideration PdM and try to develop a model that would allow us to carry out PdM process, to detect fault in the parts of automobiles and to know about the fault before its occurrence and take precautions at the earliest before breakdown of the automobile. The model will identify process and logistics variables collected during production to identify the reasons of the degradation of the automobile. The prediction of machine health can, not only significantly reduce the unexploited downtime and expensive labour costs but also ensures safe operation and optimizes the maintenance plan. We have used two ML techniques in the proposed model i.e. Binary classification and Regression. Binary classification tells about whether Fault will be encountered in the machine parts and through regression the cycles or epoch after which the fault happens will be predicted (Ali & Zain, 2019; Costello et al., 2017; Liu et al., 2015; Sherstinsky, 2020). We have also used LSTM under Regression as it can process long sequences. LSTM networks apply well to PdM domain since they are good at learning from a specific sequence. The model developed will be trained. Then later the results of the two techniques will be analysed. The results are plotted in graphs showing the credibility of the model. The fault detection of the degraded automobile part will help to solve our problem of maintenance.
PdM solves the challenges posed by Run to Failure and preventive maintenance. A model to implement PdM at the larger level is required. The model should be able to predict the failure occurrence at the right time so as to make use of the resources available effectively. The model will gather information about the health status of the machine parts, process out calculations through periodic cycles and provide effective solution for the maintenance and the faults will be diagnosed and corrected. According to the results gathered through the model, a detailed plan of action can be formulated. The model will make use of the ML techniques in order to provide the valid results.