Prediction of Machine Failure in Industry 4.0: A Hybrid CNN-LSTM Framework
The proliferation of sensing technologies such as sensors has resulted in vast amounts of time-series data being produced by machines in industrial plants and factories. There is much information available that can be used to predict machine breakdown and degradation in a given factory. The downtime...
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| Format: | Article |
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MDPI AG
2022-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/9/4221 |
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| author | Abdul Wahid John G. Breslin Muhammad Ali Intizar |
| author_facet | Abdul Wahid John G. Breslin Muhammad Ali Intizar |
| author_sort | Abdul Wahid |
| collection | DOAJ |
| description | The proliferation of sensing technologies such as sensors has resulted in vast amounts of time-series data being produced by machines in industrial plants and factories. There is much information available that can be used to predict machine breakdown and degradation in a given factory. The downtime of industrial equipment accounts for heavy losses in revenue that can be reduced by making accurate failure predictions using the sensor data. Internet of Things (IoT) technologies have made it possible to collect sensor data in real time. We found that hybrid modelling can result in efficient predictions as they are capable of capturing the abstract features which facilitate better predictions. In addition, developing effective optimization strategy is difficult because of the complex nature of different sensor data in real time scenarios. This work proposes a method for multivariate time-series forecasting for predictive maintenance (PdM) based on a combination of convolutional neural networks and long short term memory with skip connection (CNN-LSTM). We experiment with CNN, LSTM, and CNN-LSTM forecasting models one by one for the prediction of machine failures. The data used in this experiment are from Microsoft’s case study. The dataset provides information about the failure history, maintenance history, error conditions, and machine features and telemetry, which consists of information such as voltage, pressure, vibration, and rotation sensor values recorded between 2015 and 2016. The proposed hybrid CNN-LSTM framework is a two-stage end-to-end model in which the LSTM is leveraged to analyze the relationships among different time-series data variables through its memory function, and 1-D CNNs are responsible for effective extraction of high-level features from the data. Our method learns the long-term patterns of the time series by extracting the short-term dependency patterns of different time-series variables. In our evaluation, CNN-LSTM provided the most reliable and highest prediction accuracy. |
| first_indexed | 2024-03-10T04:22:45Z |
| format | Article |
| id | doaj.art-4cdba807d35846fd85017eae6eea27fc |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T04:22:45Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-4cdba807d35846fd85017eae6eea27fc2023-11-23T07:46:00ZengMDPI AGApplied Sciences2076-34172022-04-01129422110.3390/app12094221Prediction of Machine Failure in Industry 4.0: A Hybrid CNN-LSTM FrameworkAbdul Wahid0John G. Breslin1Muhammad Ali Intizar2Data Science Institute (DSI), National University of Ireland Galway, H91 TK33 Galway, IrelandData Science Institute (DSI), National University of Ireland Galway, H91 TK33 Galway, IrelandSchool of Electronic Engineering, Dublin City University, D09 W6Y4 Dublin, IrelandThe proliferation of sensing technologies such as sensors has resulted in vast amounts of time-series data being produced by machines in industrial plants and factories. There is much information available that can be used to predict machine breakdown and degradation in a given factory. The downtime of industrial equipment accounts for heavy losses in revenue that can be reduced by making accurate failure predictions using the sensor data. Internet of Things (IoT) technologies have made it possible to collect sensor data in real time. We found that hybrid modelling can result in efficient predictions as they are capable of capturing the abstract features which facilitate better predictions. In addition, developing effective optimization strategy is difficult because of the complex nature of different sensor data in real time scenarios. This work proposes a method for multivariate time-series forecasting for predictive maintenance (PdM) based on a combination of convolutional neural networks and long short term memory with skip connection (CNN-LSTM). We experiment with CNN, LSTM, and CNN-LSTM forecasting models one by one for the prediction of machine failures. The data used in this experiment are from Microsoft’s case study. The dataset provides information about the failure history, maintenance history, error conditions, and machine features and telemetry, which consists of information such as voltage, pressure, vibration, and rotation sensor values recorded between 2015 and 2016. The proposed hybrid CNN-LSTM framework is a two-stage end-to-end model in which the LSTM is leveraged to analyze the relationships among different time-series data variables through its memory function, and 1-D CNNs are responsible for effective extraction of high-level features from the data. Our method learns the long-term patterns of the time series by extracting the short-term dependency patterns of different time-series variables. In our evaluation, CNN-LSTM provided the most reliable and highest prediction accuracy.https://www.mdpi.com/2076-3417/12/9/4221production forecastingIndustry 4.0manufacturing production lineconvolutional neural network (CNN)artificial intelligence (AI)long short-term memory (LSTM) |
| spellingShingle | Abdul Wahid John G. Breslin Muhammad Ali Intizar Prediction of Machine Failure in Industry 4.0: A Hybrid CNN-LSTM Framework Applied Sciences production forecasting Industry 4.0 manufacturing production line convolutional neural network (CNN) artificial intelligence (AI) long short-term memory (LSTM) |
| title | Prediction of Machine Failure in Industry 4.0: A Hybrid CNN-LSTM Framework |
| title_full | Prediction of Machine Failure in Industry 4.0: A Hybrid CNN-LSTM Framework |
| title_fullStr | Prediction of Machine Failure in Industry 4.0: A Hybrid CNN-LSTM Framework |
| title_full_unstemmed | Prediction of Machine Failure in Industry 4.0: A Hybrid CNN-LSTM Framework |
| title_short | Prediction of Machine Failure in Industry 4.0: A Hybrid CNN-LSTM Framework |
| title_sort | prediction of machine failure in industry 4 0 a hybrid cnn lstm framework |
| topic | production forecasting Industry 4.0 manufacturing production line convolutional neural network (CNN) artificial intelligence (AI) long short-term memory (LSTM) |
| url | https://www.mdpi.com/2076-3417/12/9/4221 |
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