Abstract
Industry 4.0 the proclaimed fourth industrial revolution is unfolding at the moment. It is characterized by interconnectedness and vast amounts of available information. Industrial production has evolved enormously over the last centuries due to modern instruments. Hence issue of the instrument failure is very paramount in any industry. Even if one machine fails it halts the whole production. Overall, it may cost us with more man-hours, project delay, process latency and all this sums up as a huge loss. The life of the instruments should be taken care by continuously monitoring its health. Any faulty or unnatural disturbance in usage of the instrument may lead to its failure. Every instrument needs proper maintenance, even with the slight negligence towards the anomaly it may lead to instrument failure. In, predictive maintenance historic data is utilized and analyzed with the help of advance analytics and modelling techniques using Machine learning, moreover we can predict failures and can schedule the maintenance beforehand and predict failure in advance. With the help of relevant sensor dataset, we can estimate the remaining runtime of the instruments. This maintenance approach helps to lower the costs which are incurred due to system shut downs. It also ease the scheduling and maintenance activities.In this work, three different industrial case studies are considered like shell and tube type heat exchanger, plate type heat exchanger, and semiconductor manufacturing process.Here the predictive maintenance is carried out for heat exchanger by utilizing the concept of multi linear regression and time series analysis. For the semiconductor manufacturing dataset, support vector machine algorithm is implemented to find out the good and bad quality of semiconductor production slots.
Highlights
In the environment of Industry 4.0, proper cost allocation and time management is the primary concern to lead in this competitive Industry [10]
In conventional method of maintenance i.e., Preventive Maintenance unplanned downtimes resulting in the reduction of output, which gradually impacting the direct loss of profit
21st century, plants are increasingly turning towards machine learning (ML) to recognize patterns in sensor data
Summary
In the environment of Industry 4.0, proper cost allocation and time management is the primary concern to lead in this competitive Industry [10]. In early days before the digitalization preventive maintenance technique was used, were-in equipment was kept running in the process until failure. This was not a cost saving and time efficient method [11]. A lot of sensor data is recorded, and it plays an important role in predictive analysis Using this recorded data and machine learning algorithms like Linear/Multi Linear Regression [1], Logistic Regression [2], Time-Series Forecasting [3], Cluster Analysis, etc we can build a model which will help us with monitoring the health of the equipment and forecasting the future results well in advance and apparently helping us to schedule a
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