Machine learning based anomaly detection and diagnosis method of spinning equipment driven by spectrogram data

2021 ◽  
pp. 1-10
Author(s):  
Chen Shen ◽  
Bing Chen ◽  
Lianqing Yu ◽  
Fei Fan
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Diagnosis Method ◽  
Detection And Diagnosis

scholarly journals Anomaly Detection and Diagnosis In Manufacturing Systems

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Kalyani Zope ◽  
Kuldeep Singh ◽  
Sri Harsha Nistala ◽  
Arghya Basak ◽  
Pradeep Rathore ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Anomaly Detection ◽  
Manufacturing Systems ◽  
Predictive Maintenance ◽  
Sensor Data ◽  
Statistical Techniques ◽  
Industrial Systems ◽  
Learning Techniques ◽  
Detection And Diagnosis

Multivariate sensor data collected from manufacturing and process industries represents actual operational behavior and can be used for predictive maintenance of the plants. Anomaly detection and diagnosis, that forms an integral part of predictive maintenance, in industrial systems is however challenging due to their complex behavior, interactions among sensors, corrective actions of control systems and variability in anomalous behavior. While several statistical techniques for anomaly detection have been in use for a long time, these are not particularly suited for temporal (or contextual) anomalies that are characteristic of multivariate time series sensor data. On the other hand, several machine learning and deep learning techniques for anomaly detection gained significant interest in the recent years. Further, anomaly diagnosis that involves localization of the faults did not receive much attention. In this work, we compare the anomaly detection and diagnosis capabilities, in semi-supervised mode, of several statistical, machine learning and deep learning techniques on two systems viz. the interacting quadruple tank system and the continuous stirred tank reactor (CSTR) system both of which are representative of the complexity of large industrial systems. The techniques studied include principal component analysis (PCA), Mahalanobis distance (MD), one-class support vector machine (OCSVM), isolation forest, elliptic envelope, dense auto-encoder and long short term memory auto-encoder (LSTM AE). The study revealed that MD and LSTM-AE have the highest anomaly detection capability, followed closely by PCA and OCSVM. The above techniques also exhibited good diagnosis capability. The study indicates that statistical techniques in spite of their simplicity could be as powerful as machine learning and deep learning techniques, and may be considered for anomaly detection and diagnosis in manufacturing systems.

An Anomaly Detection and Diagnosis Method Based on Real-Time Health Monitoring for Progressive Stamping Processes

2015 ◽  
Author(s):  
Huanyi Shui ◽  
Xiaoning Jin ◽  
Jun Ni
Keyword(s):  
Anomaly Detection ◽  
Real Time ◽  
Health Monitoring ◽  
System Level ◽  
Practical Significance ◽  
Detection And Identification ◽  
Diagnosis Method ◽  
Detection And Diagnosis ◽  
Step Control

Progressive stamping processes have been applied to fabricate an extended range of products from centimeter-scale parts to meter-scale parts. The quality of stamped products may vary and be out of specification due to various anomalies during manufacturing process. Therefore, an effective online health monitoring and fault diagnosis technique is of great practical significance. This paper develops a two-stage systematic approach to enhance the fault detection and fault identification capability for the progressive stamping process with aggregated system-level tonnage signals. The first stage uses a combined Haar transform and power spectrum analysis to map features extracted from aggregated signals to individual operations. The second stage develops a two-step control chart strategy for anomaly detection and identification. The proposed method can improve the monitoring effectiveness and the quality assessment of individual operations based on an aggregated tonnage signal especially when single working range of different operations in the multi-station system are highly overlapped. The results show the method efficacy of quick and accurate anomaly detection and identification in real time.

scholarly journals OutlierNets: Highly Compact Deep Autoencoder Network Architectures for On-Device Acoustic Anomaly Detection

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4805
Author(s):  
Saad Abbasi ◽  
Mahmoud Famouri ◽  
Mohammad Javad Shafiee ◽  
Alexander Wong
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Network Architectures ◽  
Design Exploration ◽  
Convolutional Autoencoder ◽  
Acoustic Anomaly ◽  
Human Operators ◽  
Computational Resources

Human operators often diagnose industrial machinery via anomalous sounds. Given the new advances in the field of machine learning, automated acoustic anomaly detection can lead to reliable maintenance of machinery. However, deep learning-driven anomaly detection methods often require an extensive amount of computational resources prohibiting their deployment in factories. Here we explore a machine-driven design exploration strategy to create OutlierNets, a family of highly compact deep convolutional autoencoder network architectures featuring as few as 686 parameters, model sizes as small as 2.7 KB, and as low as 2.8 million FLOPs, with a detection accuracy matching or exceeding published architectures with as many as 4 million parameters. The architectures are deployed on an Intel Core i5 as well as a ARM Cortex A72 to assess performance on hardware that is likely to be used in industry. Experimental results on the model’s latency show that the OutlierNet architectures can achieve as much as 30x lower latency than published networks.

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