International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 05 | May 2023
p-ISSN: 2395-0072
www.irjet.net
Developing Algorithm for Fault Detection and Classification for DC Motor Using Predictive Maintenance Karan Jagdish Chanchlani1, Dr. Dipesh H Makwana2 1Student, Instrumentation and Control Dept, LD College of Engineering, Ahmedabad, Gujarat,
India
2Associate Professor, Instrumentation and Control Dept, LD College of Engineering, Ahmedabad, Gujarat,
India
---------------------------------------------------------------------***--------------------------------------------------------------------Abstract - Maximizing production quantity and quality while retaining the lowest possible production and maintenance costs is the primary goal of an industrial facility or utility. This can only be accomplished if the plant operates effectively; hence, regular maintenance is required. There are numerous tactics, but industry 4.0 predictive maintenance is among the most important. Predictive maintenance assists in foreseeing faults that will happen following data processing and alerts the operator. Predictive maintenance technology increases the efficiency of plant maintenance, lowers maintenance costs, and extends the useful life of the equipment. This study examines the design and development of the predictive maintenance algorithm for anticipating both healthy and defective data from real-time data from DC Motor Hardware
Figure 1: Types of Maintenance (Ref: RealPars) The least ideal option is reactive maintenance, which entails repairing equipment after it has failed. Preventive maintenance, on the other hand, is performing routine maintenance in order to avoid breakdowns. Predictive maintenance makes use of modern monitoring technologies to predict possible equipment breakdowns, allowing maintenance work to be scheduled ahead of time. Industrial organisations may ensure that their equipment functions at maximum capacity by selecting the appropriate maintenance strategy, resulting in greater productivity and reduced downtime.
Key Words: Predictive Maintenance, DC Motor, Industry 4.0, Tree, MATLAB
1.INTRODUCTION Since the early days of the industrial revolution, maintenance has been primarily reactive, responding to equipment breakdowns, resulting in unexpected downtime and high maintenance costs. However, with technological advancements, maintenance practises have become more advanced and proactive. Nowadays, maintenance tasks are performed proactively based on predetermined schedules and utilise modern monitoring and diagnostic tools [1]. The least ideal option is reactive maintenance, which includes repairing equipment after it has failed. Preventive maintenance, on the other hand, is performing routine maintenance to avoid breakdowns. Predictive maintenance uses advanced monitoring technology to detect possible equipment breakdowns, allowing maintenance work to be scheduled ahead of time. Industrial organisations may ensure that their equipment functions at maximum capacity by selecting the optimal maintenance technique, resulting in greater productivity and reduced downtime.
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Predictive maintenance is important in Industry 4.0, the fourth industrial revolution marked by the incorporation of digital technologies into industrial processes show in fig 2 [2]. To optimise industrial processes, massive amounts of data from sensors and other sources are collected and analysed in Industry 4.0. In this context, predictive maintenance combines contemporary sensors and analytical technologies to monitor system status and forecast repair needs in real time. This preventive strategy decreases the likelihood of accidental interruption. One of the benefits of predictive maintenance in Industry 4.0 is the possibility of shifting from reactive to proactive maintenance operations. Manufacturers can reduce operating expenses, boost equipment efficiency, and improve total output by estimating future failures.
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