scholarly journals Anomaly Detection for Predictive Maintenance in Industry 4.0- A survey

2020 ◽  
Vol 170 ◽  
pp. 02007
Author(s):  
Pooja Kamat ◽  
Rekha Sugandhi
Keyword(s):  
Anomaly Detection ◽  
Manufacturing Industry ◽  
Remaining Useful Life ◽  
Predictive Maintenance ◽  
Financial Loss ◽  
Lower Productivity ◽  
Useful Life ◽  
Primary Focus ◽  
Periodic Replacement ◽  
Detection Strategies

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.

scholarly journals Deep learning-based anomaly-onset aware remaining useful life estimation of bearings

2021 ◽  
Vol 7 ◽  
pp. e795
Author(s):  
Pooja Vinayak Kamat ◽  
Rekha Sugandhi ◽  
Satish Kumar
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Short Term Memory ◽  
Rotating Machinery ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Learning Models ◽  
Vibration Data ◽  
Degradation Data ◽  
Useful Life

Remaining Useful Life (RUL) estimation of rotating machinery based on their degradation data is vital for machine supervisors. Deep learning models are effective and popular methods for forecasting when rotating machinery such as bearings may malfunction and ultimately break down. During healthy functioning of the machinery, however, RUL is ill-defined. To address this issue, this study recommends using anomaly monitoring during both RUL estimator training and operation. Essential time-domain data is extracted from the raw bearing vibration data, and deep learning models are used to detect the onset of the anomaly. This further acts as a trigger for data-driven RUL estimation. The study employs an unsupervised clustering approach for anomaly trend analysis and a semi-supervised method for anomaly detection and RUL estimation. The novel combined deep learning-based anomaly-onset aware RUL estimation framework showed enhanced results on the benchmarked PRONOSTIA bearings dataset under non-varying operating conditions. The framework consisting of Autoencoder and Long Short Term Memory variants achieved an accuracy of over 90% in anomaly detection and RUL prediction. In the future, the framework can be deployed under varying operational situations using the transfer learning approach.

scholarly journals Integration of FMECA and statistical snalysis for predictive maintenance

2021 ◽  
Vol 2 (1) ◽  
pp. 33
Author(s):  
Rabia Ghani
Keyword(s):  
Production Line ◽  
Manufacturing Industry ◽  
Failure Modes ◽  
Remaining Useful Life ◽  
Predictive Maintenance ◽  
Time To Failure ◽  
Failure Data ◽  
Criticality Analysis

<p>The estimation of time-to-failure of machines is of utmost importance in the Manufacturing Industry. As the world is moving towards Industry 4.0, it is high time that we progress from the traditional methods, where we wait for a breakdown to occur, to the prognostics based methods. It is the need of the era to be aware of any incident before it occurs. This study provides application of Statistical-based Predictive maintenance. A BOPP Production line has been considered as a case study for this research. Since the inception of the line in 2013, it is evident that 60% of breakdowns are due to lack of maintenance and timely replacement of bearings. Therefore, the research is based on the application of FMECA (Failure Modes, Effects and Criticality Analysis) to determine which bearing in the production line is most prone to failure and determination of which statistical model best fits the failure data of the most critical bearing. The result provides the best distribution fit for the failure data and the fit can be utilized for further study on RUL (Remaining Useful Life) of the bearing through Bayesian Inference.</p>

A Big Data implementation of the MANTIS reference architecture for predictive maintenance

2019 ◽  
Vol 233 (10) ◽  
pp. 1361-1375
Author(s):  
Felix Larrinaga ◽  
Javier Fernandez-Anakabe ◽  
Ekhi Zugasti ◽  
Iñaki Garitano ◽  
Urko Zurutuza ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Remaining Useful Life ◽  
Predictive Maintenance ◽  
Reference Architecture ◽  
Implementation Support ◽  
Physical Systems ◽  
Root Cause ◽  
Hadoop Distributed File System ◽  
Useful Life

This article presents the implementation of a reference architecture for cyber-physical systems to support condition-based maintenance of industrial assets. It also focuses on describing the data analysis approach to manage predictive maintenance of clutch-brake assets fleet over the previously defined MANTIS reference architecture. Proposals for both the architecture and data analysis implementation support working on Big Data scenarios, due to the usage of related technologies, such as Hadoop Distributed File System, Kafka or Apache Spark. The techniques are (1) root cause analysis powered by attribute-oriented induction clustering and (2) remaining useful life powered by time series forecasting. The work has been conducted in a real use case within the H2020 European project MANTIS.

scholarly journals Remaining Useful Life (RUL) Prediction of Equipment in Production Lines Using Artificial Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 932
Author(s):  
Ziqiu Kang ◽  
Cagatay Catal ◽  
Bedir Tekinerdogan
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Continuous Production ◽  
Case Study Research ◽  
Remaining Useful Life ◽  
Predictive Maintenance ◽  
Production Lines ◽  
Important Concern ◽  
Proposed Model ◽  
Useful Life

Predictive maintenance of production lines is important to early detect possible defects and thus identify and apply the required maintenance activities to avoid possible breakdowns. An important concern in predictive maintenance is the prediction of remaining useful life (RUL), which is an estimate of the number of remaining years that a component in a production line is estimated to be able to function in accordance with its intended purpose before warranting replacement. In this study, we propose a novel machine learning-based approach for automating the prediction of the failure of equipment in continuous production lines. The proposed model applies normalization and principle component analysis during the pre-processing stage, utilizes interpolation, uses grid search for parameter optimization, and is built with multilayer perceptron neural network (MLP) machine learning algorithm. We have evaluated the approach using a case study research to predict the RUL of engines on NASA turbo engine datasets. Experimental results demonstrate that the performance of our proposed model is effective in predicting the RUL of turbo engines and likewise substantially enhances predictive maintenance results.

scholarly journals Data-Driven Remaining Useful Life Prediction Considering Sensor Anomaly Detection and Data Recovery

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 58336-58345 ◽  
Author(s):  
Liansheng Liu ◽  
Qing Guo ◽  
Datong Liu ◽  
Yu Peng
Keyword(s):  
Anomaly Detection ◽  
Life Prediction ◽  
Remaining Useful Life ◽  
Data Recovery ◽  
Data Driven ◽  
Useful Life

scholarly journals Semi-supervised Constrained Hidden Markov Model Using Multiple Sensors for Remaining Useful Life Prediction and Optimal Predictive Maintenance

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Xinyu Zhao ◽  
Yunyi Kang ◽  
Hao Yan ◽  
Feng Ju
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Remaining Useful Life ◽  
Predictive Maintenance ◽  
Multiple Sensors ◽  
Degradation Data ◽  
State Identification ◽  
Markov Decision ◽  
Useful Life

Remaining Useful Life (RUL) estimation is critical in many engineering systems where proper predictive maintenance is needed to increase a unit's effectiveness and reduce time and cost of repairing. Typically for such systems, multiple sensors are normally used to monitor performance, which create difficulties for system state identification. In this paper, we develop a semi-supervised left-to-right constrained Hidden Markov Model (HMM) model, which is effective in estimating the RUL, while capturing the jumps among states in condition dynamics. In addition, based on the HMM model learned from multiple sensors, we build a Partial Observable Markov Decision Process (POMDP) to demonstrate how such RUL estimation can be effectively used for optimal preventative maintenance decision making. We apply this technique to the NASA Engine degradation data and demonstrate the effectiveness of the proposed method.

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