Abstract
Maintenance and reliability professionals in the manufacturing industry have the primary goal of improving asset availability. Poor and fewer maintenance strategies can result in lower productivity of machinery. At the same time unplanned downtimes due to frequent maintenance activities can lead to financial loss. This has put organizations’ thought process into a trade-off situation to choose between extending the remaining functional life of the equipment at the risk of taking machine down (run-to-failure) or attempting to improve uptime by carrying out early and periodic replacement of potentially good parts which could have run successfully for a few more cycles. Predictive maintenance (PdM) aims to break these tradeoffs by empowering manufacturers to improve the remaining useful life of their machines and at the same time avoiding unplanned downtime and decreasing planned downtime. Anomaly detection lies at the core of PdM with the primary focus on finding anomalies in the working equipment at early stages and alerting the manufacturing supervisor to carry out maintenance activity. This paper describes the challenges in traditional anomaly detection strategies and propose a novel deep learning technique to predict abnormalities ahead of actual failure of the machinery.
Highlights
1.1 IIoT- Industrial Internet of ThingsIoT or Internet of Things has boomed in recent years
Industry 4.0 is undergoing a revolution with innovation at its core for those who wish to revamp the organizational processes in the industry [1]. This has led to the upsurge of the Industrial Internet of Things (IIoT) technology wherein industry equipment is largely driven by sensors and sensor data
The authors of this paper propose a novel deep learning technique which is proactive in terms that will “learn” and study patterns leading to anomalies and faults in machinery beforehand and proactively warn the personnel about possible issues well ahead of time
Summary
IoT or Internet of Things has boomed in recent years. IoT allows machines to communicate with each other over the Internet in real-time. One of the biggest issues faced by the manufacturing industries is their processes can identify only 20 % anomalies beforehand [4] This means that most of the time the anomalies go undetected and the industry are unable to handle them due to ineffective anomaly detection techniques. Detection is the process to identify variables or items that do not belong to an expected pattern in the same dataset and is usually unobservable to human eye. Such anomalies which we can term as early signs of failure usually result into equipment breakdown or lead to faults in the working of the equipment. Example: A worn-out roll bearing that leads to the breakdown of the machine
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