A deep LSTM autoencoder-based framework for predictive maintenance of a proton radiotherapy delivery system

Dou, T; Clasie, B.; Depauw, Nicolas; Shen, Tim; Brett, Robert; Lu, Hsiao‐Ming; Flanz, J.; Jee, Kyung‐Wook

doi:10.1016/j.artmed.2022.102387

Cited by 3 publications

(4 citation statements)

References 44 publications

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“…To address this limitation, LSTM-Autoencoders have been proposed, which combine the sequential processing capabilities of LSTMs with the feature learning capabilities of Autoencoders [52] LSTM-Autoencoders are a type of Autoencoder architecture that uses LSTM networks as the encoder and decoder parts. Combining LSTM and an Autoencoder creates a powerful architecture for sequence data processing tasks, such as anomaly detection, data denoising, and feature extraction [53,54]. The Autoencoder structure enables the model to learn a compressed representation of the data, while the LSTM part allows the model to capture the time-series dependencies and long-term patterns in the data.…”

Section: Literature Reviewmentioning

confidence: 99%

Condition Monitoring and Predictive Maintenance of Assets in Manufacturing Using LSTM-Autoencoders and Transformer Encoders

Bampoula,

Nikolakis,

Alexopoulos

2024

Sensors

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The production of multivariate time-series data facilitates the continuous monitoring of production assets. The modelling approach of multivariate time series can reveal the ways in which parameters evolve as well as the influences amongst themselves. These data can be used in tandem with artificial intelligence methods to create insight on the condition of production equipment, hence potentially increasing the sustainability of existing manufacturing and production systems, by optimizing resource utilization, waste, and production downtime. In this context, a predictive maintenance method is proposed based on the combination of LSTM-Autoencoders and a Transformer encoder in order to enable the forecasting of asset failures through spatial and temporal time series. These neural networks are implemented into a software prototype. The dataset used for training and testing the models is derived from a metal processing industry case study. Ultimately, the goal is to train a remaining useful life (RUL) estimation model.

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Section: Literature Reviewmentioning

confidence: 99%

Condition Monitoring and Predictive Maintenance of Assets in Manufacturing Using LSTM-Autoencoders and Transformer Encoders

Bampoula,

Nikolakis,

Alexopoulos

2024

Sensors

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“…Dou et al. proposed a predictive maintenance framework based on a long short-term memory-based autoencoder to detect rare anomalous machine events for a proton delivery system ( 21 ). These included QA beam pauses, clinical operational issues, and treatment interruptions.…”

Section: Introductionmentioning

confidence: 99%

“…The model was trained on a variety of deformations and anatomies which enable it to generate the 3D motion experienced by the liver of a previously unseen subject. Dou et al proposed a predictive maintenance framework based on a long short-term memory-based autoencoder to detect rare anomalous machine events for a proton delivery system (21). These included QA beam pauses, clinical operational issues, and treatment interruptions.…”

Section: Introductionmentioning

confidence: 99%

Anomaly detection in radiotherapy plans using deep autoencoder networks

Huang

Shang

et al. 2023

Front. Oncol.

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PurposeTreatment plans are used for patients under radiotherapy in clinics. Before execution, these plans are checked for safety and quality by human experts. A few of them were identified with flaws and needed further improvement. To automate this checking process, an unsupervised learning method based on an autoencoder was proposed.MethodsFirst, features were extracted from the treatment plan by human experts. Then, these features were assembled and used for model learning. After network optimization, a reconstruction error between the predicted and target signals was obtained. Finally, the questionable plans were identified based on the value of the reconstruction error. A large value of the reconstruction error indicates a longer distance from the standard distribution of normal plans. A total of 576 treatment plans for breast cancer patients were used for the test. Among them, 19 were questionable plans identified by human experts. To evaluate the performance of the autoencoder, it was compared with four baseline detection algorithms, namely, local outlier factor (LOF), hierarchical density-based spatial clustering of applications with noise (HDBSCAN), one-class support vector machine (OC-SVM), and principal component analysis (PCA).ResultsThe results showed that the autoencoder achieved the best performance than the other four baseline algorithms. The AUC value of the autoencoder was 0.9985, while the second one was 0.9535 (LOF). While maintaining 100% recall, the average accuracy and precision of the results by the autoencoder were 0.9658 and 0.5143, respectively. While maintaining 100% recall, the average accuracy and precision of the results by LOF were 0.8090 and 0.1472, respectively.ConclusionThe autoencoder can effectively identify questionable plans from a large group of normal plans. There is no need to label the data and prepare the training data for model learning. The autoencoder provides an effective way to carry out an automatic plan checking in radiotherapy.

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“…In the field of medicine, T. Dou et al [ 34 ] examined the Predictive Maintenance application of anomaly detection on proton Pencil Beam Scanning (PBS) delivery systems. LSTM-based Stacked Autoencoder (LSTM-SAE) models with a dropout rate [ 35 ] were trained and evaluated on temporal beam data including the most essential system operating parameters.…”

Section: Introductionmentioning

confidence: 99%

Implementation of a Sequence-to-Sequence Stacked Sparse Long Short-Term Memory Autoencoder for Anomaly Detection on Multivariate Timeseries Data of Industrial Blower Ball Bearing Units

Karapalidou,

Alexandris,

Antoniou

et al. 2023

Sensors

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The advent of Industry 4.0 introduced new ways for businesses to evolve by implementing maintenance policies leading to advancements in terms of productivity, efficiency, and financial performance. In line with the growing emphasis on sustainability, industries implement predictive techniques based on Artificial Intelligence for the purpose of mitigating machine and equipment failures by predicting anomalies during their production process. In this work, a new dataset that was made publicly available, collected from an industrial blower, is presented, analyzed and modeled using a Sequence-to-Sequence Stacked Sparse Long Short-Term Memory Autoencoder. Specifically the right and left mounted ball bearing units were measured during several months of normal operational condition as well as during an encumbered operational state. An anomaly detection model was developed for the purpose of analyzing the operational behavior of the two bearing units. A stacked sparse Long Short-Term Memory Autoencoder was successfully trained on the data obtained from the left unit under normal operating conditions, learning the underlying patterns and statistical connections of the data. The model was evaluated by means of the Mean Squared Error using data from the unit’s encumbered state, as well as using data collected from the right unit. The model performed satisfactorily throughout its evaluation on all collected datasets. Also, the model proved its capability for generalization along with adaptability on assessing the behavior of equipment similar to the one it was trained on.

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A deep LSTM autoencoder-based framework for predictive maintenance of a proton radiotherapy delivery system

Cited by 3 publications

References 44 publications

Condition Monitoring and Predictive Maintenance of Assets in Manufacturing Using LSTM-Autoencoders and Transformer Encoders

Condition Monitoring and Predictive Maintenance of Assets in Manufacturing Using LSTM-Autoencoders and Transformer Encoders

Anomaly detection in radiotherapy plans using deep autoencoder networks

Implementation of a Sequence-to-Sequence Stacked Sparse Long Short-Term Memory Autoencoder for Anomaly Detection on Multivariate Timeseries Data of Industrial Blower Ball Bearing Units

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