Anomaly Detection for Predictive Maintenance in Industry 4.0- A survey

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.

Application of Enhanced CPC for Load Identification, Preventive Maintenance and Grid Interpretation

“…(5) If enhanced CPC implementation uses CPC(j * i customer ) to force active, scattered, it switches 'also' active and reactive and should be reminded. (6). Complete graphs are given in [44].…”

“…Anomaly detection attracts considerable research efforts, mostly by means of analytic models [1][2][3]. Work was performed regarding various types of anomaly detectors and various objectives such as: Vacca's review book about anomaly detection on computer networks intrusion detection systems [4], Kovanen et al performed anomaly detection survey work on encrypted traffic [5], part of a book "Lecture Notes in Computer Science" by Springer, and Kamat et al performed an anomaly detection survey work for preventive maintenance [6]. Multi-channel anomaly is a more sophisticated since it requires multiplicative computation resources and sometimes a collaboration of the channels: generation of a collaborative view.…”

Context-Based Resilience in Cyber-Physical Production System

“…To ensure timely recovery actions, efficient and effective anomaly detection techniques constitute a key enabling factor. Anomaly detection has been largely investigated in recent years, covering data from several domains [5][6][7][8], including healthcare domain [9,10] and Industry 4.0 [11]. In [12], we integrated an anomaly detection approach with a CMMS (Computerised Maintenance Management System) to provide time-limited maintenance interventions.…”

Smart Anomaly Detection and Prediction for Assembly Process Maintenance in Compliance with Industry 4.0

“…According to [ 11 ], one of the key components of PdM is anomaly detection. Its principle is that it looks for anomalies in data or processes so that it can inform maintenance staff in advance of an emerging problem.…”