LSTM-based Anomaly Detection for Railway Vehicle Air-conditioning Unit using Monitoring Data

Yokouchi, Toshihide; Kondo, Minoru

doi:10.1109/iecon48115.2021.9589928

Cited by 5 publications

(4 citation statements)

References 9 publications

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“…In this report, we propose a method for training the data with a neural network incorporating LSTM as an effective method for learning time-series data. The method is divided into the following three steps [8][9] [10] shown in Fig. 2.…”

Section: Anomaly Detection Methods For Vehicle Equipmentmentioning

confidence: 99%

<b>Anomaly Detection for Railway Vehicle Equipment Using Condition Monitoring Data</b>

Yokouchi¹,

TAKASHIGE²,

Kondo³

2022

QR of RTRI

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In recent years, some railway vehicles have been equipped with condition monitoring devices, which constantly record the operating condition of railway vehicle equipment. For more effective use of condition monitoring devices, we propose an anomaly detection method for railway vehicle equipment using Long Short-Term Memory (LSTM), which is a deep learning method suitable for learning time-series data. In this paper, we apply the proposed method to data on engines and air-conditioning units recorded on vehicles in operations. Results confirmed that the anomaly score for anomalous data increases by using the proposed method, and that anomalies are detected in railway vehicle equipment before faults appear.

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Section: Anomaly Detection Methods For Vehicle Equipmentmentioning

confidence: 99%

<b>Anomaly Detection for Railway Vehicle Equipment Using Condition Monitoring Data</b>

Yokouchi¹,

TAKASHIGE²,

Kondo³

2022

QR of RTRI

Self Cite

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“…Li et al [ 14 ] analyzed the sensor data of the wind turbine with professional knowledge and, after feature extraction, uses the hyperparameter search method to train the support vector machine model to diagnose faults. Finally, Yokouchi and Kondo [ 15 ] introduced a single-class SVM model to learn the boundary of the common data space by collecting the equipment's average data and applying it to the fault detection of the equipment.…”

Section: Related Workmentioning

confidence: 99%

Light-Weighted Deep Learning Model to Detect Fault in IoT-Based Industrial Equipment

Soni

Khan

Baseer

et al. 2022

Computational Intelligence and Neuroscience

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Industry 4.0, with the widespread use of IoT, is a significant opportunity to improve the reliability of industrial equipment through problem detection. It is difficult to utilize a unified model to depict the working condition of devices in real-world industrial scenarios because of the complex and dynamic relationship between devices. The scope of this research is that it can detect equipment defects and deploys them in a natural production environment. The proposed research is describing an online detection method for system failures based on long short-term memory neural networks. In recent years, deep learning technology has taken over as the primary method for detecting faults. A neural network with a long short-term memory is used to develop an online defect detection model. Feature extraction from sensor data is done using the curve alignment method. Based on long-term memory neural networks, the fault detection model is built (LSTM). In the end, sliding window technology is used to identify and fix the problem: the model’s online detection and update. The method’s efficacy is demonstrated by experiments based on real data from power plant sensors.

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“…LSTM and its variants have been used for fault detection in recent years. Yokouchi et al [35] present a method for anomaly detection in railway vehicle air-conditioning units using LSTM networks. This approach involves learning from normal operational data to build a predictive model and then to predict normal data, establishing a distribution of prediction errors.…”

Section: Introductionmentioning

confidence: 99%

A Deep-LSTM-Based Fault Detection Method for Railway Vehicle Suspensions

Chen,

Liu,

Fan

et al. 2024

Machines

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The timely detection of faults that occur in industrial machines and components can avoid possible catastrophic machine failure, prevent large financial losses, and ensure the safety of machine operators. A solution to tackle the fault detection problem is to start with modeling the condition monitoring signals and then examine any deviation of real-time monitored data from the baseline model. The newly developed deep long short-term memory (LSTM) neural network has a high nonlinear flexibility and can simultaneously store long- and short-term memories. Thus, deep LSTM is a good option for representing underlying data-generating processes. This paper presents a deep-LSTM-based fault detection method. A goodness-of-fit criterion is innovatively used to quantify the deviation between the baseline model and the newly monitored vibrations as opposed to the mean squared value of the LSTM residual used in many reported works. A railway suspension fault detection case is studied. Benchmark studies have shown that the deep-LSTM-based fault detection method performs better than the vanilla-LSTM-based and linear-autoregression-model-based methods. Using the goodness-of-fit criterion, railway suspension faults can be better detected than when using the mean squared value of the LSTM residual.

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LSTM-based Anomaly Detection for Railway Vehicle Air-conditioning Unit using Monitoring Data

Cited by 5 publications

References 9 publications

<b>Anomaly Detection for Railway Vehicle Equipment Using Condition Monitoring Data</b>

<b>Anomaly Detection for Railway Vehicle Equipment Using Condition Monitoring Data</b>

Light-Weighted Deep Learning Model to Detect Fault in IoT-Based Industrial Equipment

A Deep-LSTM-Based Fault Detection Method for Railway Vehicle Suspensions

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