Data-driven Industrial Machine Failure Detection in Imbalanced Environments

Vuttipittayamongkol, Pattaramon; Arreeras, Tosporn

doi:10.1109/ieem55944.2022.9989673

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…In one of the other studies in the literature, the authors present a machine learning -based approach for industrial machine failure and they achieve the highest performance by using the DT classification method among the non-deep learning algorithms [53]. The selected machine learning method (DT) reached max.…”

Section: Discussionmentioning

confidence: 99%

An Efficient Approach for Automatic Fault Classification Based on Data Balance and One-Dimensional Deep Learning

Ileri,

Altun,

Narin

2024

Applied Sciences

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Predictive maintenance (PdM) is implemented to efficiently manage maintenance schedules of machinery and equipment in manufacturing by predicting potential faults with advanced technologies such as sensors, data analysis, and machine learning algorithms. This paper introduces a study of different methodologies for automatically classifying the failures in PdM data. We first present the performance evaluation of fault classification performed by shallow machine learning (SML) methods such as Decision Trees, Support Vector Machines, k-Nearest Neighbors, and one-dimensional deep learning (DL) techniques like 1D-LeNet, 1D-AlexNet, and 1D-VGG16. Then, we apply normalization, which is a scaling technique in which features are shifted and rescaled in the dataset. We reapply classification algorithms to the normalized dataset and present the performance tables in comparison with the first results we obtained. Moreover, in contrast to existing studies in the literature, we generate balanced dataset groups by randomly selecting normal data and all faulty data for all fault types from the original dataset. The dataset groups are generated with 100 different repetitions, recording performance scores for each one and presenting the maximum scores. All methods utilized in the study are similarly employed on these groups. From these scores, the use of 1D-LeNet deep learning classifiers and feature normalization resulted in achieving the highest overall accuracy and F1-score performance of 98.50% and 98.32%, respectively. As a result, the goal of this study was to develop an efficient approach for automatic fault classification, leveraging data balance, and additionally, to provide an analysis of one-dimensional deep learning and shallow machine learning-based classification methods. In light of the experimentation and comparative analysis, this study successfully achieves its stated goal by demonstrating that one-dimensional deep learning and data balance collectively emerge as the optimal approach, offering good prediction accuracy.

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Section: Discussionmentioning

confidence: 99%

An Efficient Approach for Automatic Fault Classification Based on Data Balance and One-Dimensional Deep Learning

Ileri,

Altun,

Narin

2024

Applied Sciences

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“…For all the datasets, Random UnderSampling (RUS) [69,70] is applied to balance the class distribution by making the cardinality of the majority class comparable to that of the minority class. RUS has demonstrated its effectiveness in diverse fields where the class imbalance is common, such as astrophysics [71,72], geoscience [73], and industrial informatics [74][75][76]. The algorithm randomly selects and removes observations from the majority class until it achieves the desired equilibrium between the two classes.…”

Section: Class Unbalancementioning

confidence: 99%

Predicting Machine Failures from Multivariate Time Series: An Industrial Case Study

Pinciroli Vago,

Forbicini,

Fraternali

2024

Machines

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Non-neural machine learning (ML) and deep learning (DL) are used to predict system failures in industrial maintenance. However, only a few studies have assessed the effect of varying the amount of past data used to make a prediction and the extension in the future of the forecast. This study evaluates the impact of the size of the reading window and of the prediction window on the performances of models trained to forecast failures in three datasets of (1) an industrial wrapping machine working in discrete sessions, (2) an industrial blood refrigerator working continuously, and (3) a nitrogen generator working continuously. A binary classification task assigns the positive label to the prediction window based on the probability of a failure to occur in such an interval. Six algorithms (logistic regression, random forest, support vector machine, LSTM, ConvLSTM, and Transformers) are compared on multivariate time series. The dimension of the prediction windows plays a crucial role and the results highlight the effectiveness of DL approaches in classifying data with diverse time-dependent patterns preceding a failure and the effectiveness of ML approaches in classifying similar and repetitive patterns preceding a failure.

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Prediction of Workpiece Film Thickness via Multi-Region Segmented Model of Painting Process Parameters

Liao,

Lin,

et al. 2023

2023 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

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Data-driven Industrial Machine Failure Detection in Imbalanced Environments

Cited by 4 publications

References 17 publications

An Efficient Approach for Automatic Fault Classification Based on Data Balance and One-Dimensional Deep Learning

An Efficient Approach for Automatic Fault Classification Based on Data Balance and One-Dimensional Deep Learning

Predicting Machine Failures from Multivariate Time Series: An Industrial Case Study

Prediction of Workpiece Film Thickness via Multi-Region Segmented Model of Painting Process Parameters

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