Unsupervised Anomaly Detection of Industrial Robots Using Sliding-Window Convolutional Variational Autoencoder

Chen, Tingting; Liu, Xueping; Xia, Bizhong; Wang, Wei; Lai, Yongzhi

doi:10.1109/access.2020.2977892

Cited by 132 publications

(64 citation statements)

References 23 publications

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“…This method divides sequences into many overlapping subsequences and then calculates the correlations among the subsequence data in a window by a given algorithm. Chen et al [21] proposed a sliding window convolutional differential autoencoder that can detect the anomalies of multivariate time-series in time and space. When processing multidimensional time-series, we also need to consider the characteristics of the data itself in addition to the correlation between series.…”

Section: Related Workmentioning

confidence: 99%

A Novel Multivariate Time-Series Anomaly Detection Approach Using an Unsupervised Deep Neural Network

2021

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With the development of hardware technology, we can collect increasingly reliable time series data, in which time series anomaly detection is an important task to find problems in time and avoid risks. It is not easy to establish a multivariate time series anomaly detection system, because the collected data not only have different attributes, scales, and characteristic information but also have horizontal and vertical connections among these data collected by various sensors. In addition, there is no clear boundary regarding whether the data are abnormal, and there is currently no unified definition of multidimensional timeseries anomalies. Recently, deep learning methods have shown outstanding advantages in the processing of multidimensional time series. In this paper, we propose a definition of point anomalies in multivariate time series and an unsupervised deep learning method, the multilayer convolutional recurrent autoencoded anomaly detector (MCRAAD), which is used to detect anomalies in multivariate time series. We calculate the feature matrix sequence through the data in the sliding window, extract the characteristics of the feature matrix sequence by a multilayer convolutional encoder, obtain the time relations in the feature matrix by using several ConvLSTM units, and finally reconstruct the feature matrix sequence with the convolutional decoder to predict the self-feature matrix. In addition, we propose a threshold setting method to assist with the determination of anomalies. Finally, we test our model on synthetic datasets and a real dataset of house monitoring. The results of experiments show that our method is superior to these basic models in detecting ability and robustness. This model also provides an effective method for multivariate time-series anomaly detection in real life.

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Section: Related Workmentioning

confidence: 99%

A Novel Multivariate Time-Series Anomaly Detection Approach Using an Unsupervised Deep Neural Network

2021

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“…By coping with electrocardiography (ECG) data, an ensemble of autoencoders joined by the sparse connections of recurrent neural networks (RNNs) was developed [36]. CAE and LSTM AE that utilized a two-stage sliding window strategy for time series data preprocessing showed successful results for IoT data [37], while a sliding-window convolutional VAE was adopted to capture spatio-temporal patterns and detect anomalies in real-time for industrial robots [38]. However, most previous works merely average the reconstruction error and employ it as an anomaly score.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised Abnormal Sensor Signal Detection With Channelwise Reconstruction Errors

Kwak

Kim

2021

IEEE Access

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Detecting an anomaly in multichannel signal data is a challenging task in various domains. It should take into account the cross-channel relationship and temporal relationship within each channel. Moreover, the signal data is high-dimensional and making it difficult to gather sufficient abnormal labels. Consequently, unsupervised reconstruction-based anomaly detection methods have been applied successfully in many studies. However, they lose valuable channel information inherent in the reconstruction errors by merely averaging the errors for both the channel and time, then consider the average value as an anomaly score. In this study, we propose a method to explicitly employ channelwise reconstruction errors as a feature to detect abnormal signals. After a convolutional autoencoder produces the channelwise reconstruction errors, a machine learning anomaly detection model aggregates the errors as an anomaly score. To demonstrate the effectiveness and applicability of the proposed model, we conduct experiments using simulated data and real-world automobile data. The results show that the proposed method remarkably enhances the detectability compared to the simple average of the reconstruction errors. The reconstruction errors of abnormal and normal channels are shown to be different; therefore, it can be considered as an appropriate feature for anomaly detection. The best performance is obtained by using local outlier factors in the following anomaly detection model.

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“…Fault detection in an industrial environment has always been a challenging task [20], [21], [22], [23], [24]. Due to the issues related to interoperability and communication between different devices, collecting a large dataset in such an environment is not an easy task.…”

Section: State Of the Artmentioning

confidence: 99%

Fault detection and classification in Industrial IoT in case of missing sensor data

Dzaferagic¹,

Marchetti²,

Macaluso³

2021

Preprint

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This paper addresses the issue of reliability in Industrial Internet of Things (IIoT) in case of missing sensors measurements due to network or hardware problems. We propose to support the fault detection and classification modules, which are the two critical components of a monitoring system for IIoT, with a generative model. The latter is responsible of imputing missing sensor measurements so that the monitoring system performance is robust to missing data. In particular, we adopt Generative Adversarial Networks (GANs) to generate missing sensor measurements and we propose to fine-tune the training of the GAN based on the impact that the generated data have on the fault detection and classification modules. We conduct a thorough evaluation of the proposed approach using the extended Tennessee Eastman Process dataset. Results show that the GAN-imputed data mitigate the impact on the fault detection and classification even in the case of persistently missing measurements from sensors that are critical for the correct functioning of the monitoring system.

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Unsupervised Anomaly Detection of Industrial Robots Using Sliding-Window Convolutional Variational Autoencoder

Cited by 132 publications

References 23 publications

A Novel Multivariate Time-Series Anomaly Detection Approach Using an Unsupervised Deep Neural Network

A Novel Multivariate Time-Series Anomaly Detection Approach Using an Unsupervised Deep Neural Network

Unsupervised Abnormal Sensor Signal Detection With Channelwise Reconstruction Errors

Fault detection and classification in Industrial IoT in case of missing sensor data

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