The experimental application of popular machine learning algorithms on predictive maintenance and the design of IIoT based condition monitoring system

Çakır, Mustafa; Güvenç, Mehmet Ali; Mıstıkoğlu, Selçuk

doi:10.1016/j.cie.2020.106948

Cited by 100 publications

(43 citation statements)

References 43 publications

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“…The PCA is an unsupervised linear method that allows reducing the dimension of a correlated data set to a linear space of unrelated indicators while preserving the greatest possible variance. The application of the PCA results in the extraction of a new set of features that are sorted according to the cumulative variance that preserves and are known as principal components [5,11,42]. On the other hand, the LDA is a supervised linear method that allows a dimensionality reduction by extracting a new set of features, in which the maximization of the data separability is achieved for a C number of considered classes.…”

Section: Machine Learning-based Feature Reductionmentioning

confidence: 99%

“…The IM faults occurrence is commonly produced by electrical or mechanical stresses that finally produce damages over the mechanical or electrical parts of the IM. Thus, within the most common faults that influence the IM operation are those damages that affect the stator, rotor, and the bearing elements [7][8][9]; specifically, the occurrence of faults in IMs is mainly associated with these elements (stator, rotor, and bearings) representing around 32%, 10%, and 40%, respectively [10,11]. That is, a high percentage of fault occurrence in IMs is produced by its associated bearings; consequently, bearings play an important key role that may affect the operation, reliability, and efficiency of IMs.…”

Section: Introductionmentioning

confidence: 99%

“…Sci. 2021,11, 8033 15 of 20 features estimated from different signals in different domains, and its fusion and compression through the LDA can lead to a wide range of operating frequencies that, in industrial environments, cover the common ranges of rotating speeds for any considered electrical machine.…”

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confidence: 99%

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Condition Monitoring Method for the Detection of Fault Graduality in Outer Race Bearing Based on Vibration-Current Fusion, Statistical Features and Neural Network

et al. 2021

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Bearings are the elements that allow the rotatory movement in induction motors, and the fault occurrence in these elements is due to excessive working conditions. In induction motors, electrical erosion remains the most common phenomenon that damages bearings, leading to incipient faults that gradually increase to irreparable damages. Thus, condition monitoring strategies capable of assessing bearing fault severities are mandatory to overcome this critical issue. The contribution of this work lies in the proposal of a condition monitoring strategy that is focused on the analysis and identification of different fault severities of the outer race bearing fault in an induction motor. The proposed approach is supported by fusion information of different physical magnitudes and the use of Machine Learning and Artificial Intelligence. An important aspect of this proposal is the calculation of a hybrid-set of statistical features that are obtained to characterize vibration and stator current signals by its processing through domain analysis, i.e., time-domain and frequency-domain; also, the fusion of information of both signals by means of the Linear Discriminant Analysis is important due to the most discriminative and meaningful information is retained resulting in a high-performance condition characterization. Besides, a Neural Network-based classifier allows validating the effectiveness of fusion information from different physical magnitudes to face the diagnosis of multiple fault severities that appear in the bearing outer race. The method is validated under an experimental data set that includes information related to a healthy condition and five different severities that appear in the outer race of bearings.

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Section: Machine Learning-based Feature Reductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Condition Monitoring Method for the Detection of Fault Graduality in Outer Race Bearing Based on Vibration-Current Fusion, Statistical Features and Neural Network

et al. 2021

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“…Some of them focus on challenges and reliability aspect of PdM in their study [6], while others focus only on the cost and economic aspect [2,15,19]. While there are studies done for Industrial and IoT devices [10,11,12,14,16], there are only a handful studies available for cash dispenser devices [21] wherein only event/error logs are considered as variables to perform Time Series Classification. Strong indicators of failures could be gleaned out from service history, metrics, transactions, etc.…”

Section: Related Workmentioning

confidence: 99%

Predictive Maintenance of Cash Dispenser Using a Cognitive Prioritization Model

Pradhan

Mahamun

2021

Teh. glas. (Online)

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In this technical paper, we address the issue of predicting cash dispenser (addressed as ‘Device’ henceforth) failure by harnessing the power of humungous data from service history, logs, metrics, transactions, and plausible environmental factors. This study helps increase device availability, enhanced customer experience, manage risk & compliance and revenue growth. It also helps reduce maintenance cost, travel cost, labour cost, downtime, repair duration and increase meantime between failures (MTBF) of individual components. This study uses a cognitive prioritization model which entails the following at its core; a) Machine Learning engineered features with highest influence on machine failure, b) Observation Windows, Transition Windows and Prediction Windows to accommodate various business processes and service planning delivery windows, and c) A forward-looking evaluation of emerging patterns to determine failure prediction score that is prioritized by business impact, for a predefined time window in the future. The model not only predicts failure score for the devices to be serviced, but it also reduces the service miss impact for the prediction windows.

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“…This can reduce maintenance, and thus production costs, by assessing the current condition of the equipment and estimating its remaining useful life (RUL). Towards that end, the employment of artificial intelligence (AI) techniques, and in particular machine learning (ML) approaches, capable of analyzing large-scale data sets and detecting underlying patterns, can enable proactive decision-making, such as in the context of predictive maintenance [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Model for Predictive Maintenance in Cyber-Physical Production Systems Using LSTM Autoencoders

Bampoula

Siaterlis

Nikolakis

et al. 2021

Sensors

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Condition monitoring of industrial equipment, combined with machine learning algorithms, may significantly improve maintenance activities on modern cyber-physical production systems. However, data of proper quality and of adequate quantity, modeling both good operational conditions as well as abnormal situations throughout the operational lifecycle, are required. Nevertheless, this is difficult to acquire in a non-destructive approach. In this context, this study investigates an approach to enable a transition from preventive maintenance activities, that are scheduled at predetermined time intervals, into predictive ones. In order to enable such approaches in a cyber-physical production system, a deep learning algorithm is used, allowing for maintenance activities to be planned according to the actual operational status of the machine and not in advance. An autoencoder-based methodology is employed for classifying real-world machine and sensor data, into a set of condition-related labels. Real-world data collected from manufacturing operations are used for training and testing a prototype implementation of Long Short-Term Memory autoencoders for estimating the remaining useful life of the monitored equipment. Finally, the proposed approach is evaluated in a use case related to a steel industry production process.

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The experimental application of popular machine learning algorithms on predictive maintenance and the design of IIoT based condition monitoring system

Cited by 100 publications

References 43 publications

Condition Monitoring Method for the Detection of Fault Graduality in Outer Race Bearing Based on Vibration-Current Fusion, Statistical Features and Neural Network

Condition Monitoring Method for the Detection of Fault Graduality in Outer Race Bearing Based on Vibration-Current Fusion, Statistical Features and Neural Network

Predictive Maintenance of Cash Dispenser Using a Cognitive Prioritization Model

A Deep Learning Model for Predictive Maintenance in Cyber-Physical Production Systems Using LSTM Autoencoders

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