scholarly journals Sensor and Component Fault Detection and Diagnosis for Hydraulic Machinery Integrating LSTM Autoencoder Detector and Diagnostic Classifiers

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 433
Author(s):  
Ahlam Mallak ◽  
Madjid Fathi
Keyword(s):  
Fault Detection ◽  
Fault Detection And Diagnosis ◽  
Behavioral Changes ◽  
Sensor Faults ◽  
Hydraulic Systems ◽  
Hydraulic Test ◽  
Economic Implications ◽  
Hydraulic Machinery ◽  
Detection And Diagnosis ◽  
Two Phases

Anomaly occurrences in hydraulic machinery might lead to massive system shut down, jeopardizing the safety of the machinery and its surrounding human operator(s) and environment, and the severe economic implications following the faults and their associated damage. Hydraulics are mostly placed in ruthless environments, where they are consistently vulnerable to many faults. Hence, not only are the machines and their components prone to anomalies, but also the sensors attached to them, which monitor and report their health and behavioral changes. In this work, a comprehensive applicational analysis of anomalies in hydraulic systems extracted from a hydraulic test rig was thoroughly achieved. First, we provided a combination of a new architecture of LSTM autoencoders and supervised machine and deep learning methodologies, to perform two separate stages of fault detection and diagnosis. The two phases were condensed by—the detection phase using the LSTM autoencoder. Followed by the fault diagnosis phase represented by the classification schema. The previously mentioned framework was applied to both component and sensor faults in hydraulic systems, deployed in the form of two in-depth applicational experiments. Moreover, a thorough literature review of related work from the past decade, for autoencoders related fault detection and diagnosis in hydraulic systems, was successfully conducted in this study.

scholarly journals Sensor and Component Fault Detection and Diagnosis for Hydraulic Machinery Integrating LSTM Autoencoder Detector and Diagnostic Classifiers

2020 ◽  
Author(s):  
Ahlam Mallak ◽  
Madjid Fathi
Keyword(s):  
Fault Detection ◽  
Fault Detection And Diagnosis ◽  
Behavioral Changes ◽  
Hydraulic Systems ◽  
Hydraulic Test ◽  
Economic Implications ◽  
Hydraulic Machinery ◽  
Detection And Diagnosis ◽  
Two Phases ◽  
Two Stages

Anomaly occurrences in hydraulic machinery may lead to massive systems shut down, jeopardizing the safety of the machinery and its surrounding human operator(s) and environment, and the severe economic implications succeeding the faults and their associated damage. Hydraulics are mostly placed in ruthless environments, where they are consistently vulnerable to many faults. Hence, not only the machines and their components are prone to anomalies, but also the sensors attached to them, which monitor and report their health and behavioral changes. In this work, a comprehensive applicational analysis of anomalies in hydraulic systems extracted from a hydraulic test rig is thoroughly achieved. Firstly, we provided a combination of a new architecture of LSTM autoencoders and supervised machine and deep learning methodologies to perform two separate stages of fault detection and diagnosis. The two phases are condensed by: the detection phase using the LSTM autoencoder. Followed by the fault diagnosis phase represented by the classification schema. The previously mentioned framework is applied to both component and sensor faults in hydraulic systems, deployed in the form of two in-depth applicational experiments. Moreover, a thorough literature review of the past decade related work for the two stages separately is successfully conducted in this paper.

Investigating Fault Detection and Diagnosis in a Hydraulic Pitch System Using a State Augmented EKF-Approach

2019 ◽  
Author(s):  
Magnus F. Asmussen ◽  
Henrik C. Pedersen ◽  
Lina Lilleengen ◽  
Andreas Larsen ◽  
Thomas Farsakoglou
Keyword(s):  
Kalman Filter ◽  
Fault Detection ◽  
Extended Kalman Filter ◽  
Fault Detection And Diagnosis ◽  
Sensor Faults ◽  
System Parameters ◽  
Power Regulation ◽  
Pitch System ◽  
Detection And Diagnosis ◽  
Drop Outs

Abstract Pitch systems impose an important part of today’s wind turbines, where they are both used for power regulation and serve as part of a turbines safety system. Any failure on a pitch system is therefore equal to an increase in downtime of the turbine and should hence be avoided. By implementing a Fault Detection and Diagnosis (FDD) scheme faults may be detected and estimated before resulting in a failure, thus increasing the availability and aiding in the maintenance of the wind turbine. The focus of this paper is therefore on the development of a FDD algorithm to detect leakage and sensor faults in a fluid power pitch system. The FDD algorithm is based on a State Augmented Extended Kalman Filter (SAEKF) and a bank of observers, which is designed utilizing an experimentally validated model of a pitch system. The SAEKF is designed to detect and estimate both internal and external leakage faults, while also estimating the unknown external load on the system, and the bank of observers to detect sensor drop-outs. From simulation it is found that the SAEKF may detect both abrupt and evolving internal and external leakages, while being robust towards noise and variation in system parameters. Similar it is found that the scheme is able to detect sensor drop-outs, but is less robust towards this.

scholarly journals Sensor Fault Detection and Diagnosis Method for AHU Using 1-D CNN and Clustering Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-20 ◽  
Author(s):  
Jingjing Liu ◽  
Min Zhang ◽  
Hai Wang ◽  
Wei Zhao ◽  
Yan Liu
Keyword(s):  
Fault Detection ◽  
Clustering Analysis ◽  
Fault Detection And Diagnosis ◽  
Sensor Faults ◽  
Control Loop ◽  
One Dimensional ◽  
Air Handling Unit ◽  
Sensor Fault Detection ◽  
Diagnosis Method ◽  
Detection And Diagnosis

This paper presents a fault detection and diagnosis (FDD) method, which uses one-dimensional convolutional neural network (1-D CNN) and WaveCluster clustering analysis to detect and diagnose sensor faults in the supply air temperature (Tsup) control loop of the air handling unit. In this approach, 1-D CNN is employed to extract man-guided features from raw data, and the extracted features are analyzed by WaveCluster clustering. The suspicious sensor faults are indicated and categorized by denoting clusters. Moreover, the Tc acquittal procedure is introduced to further improve the accuracy of FDD. In validation, false alarm ratio and missing diagnosis ratio are mainly used to demonstrate the efficiency of the proposed FDD method. Results show that the abrupt sensor faults in Tsup control loop can be efficiently detected and diagnosed, and the proposed method is equipped with good robustness within the noise range of 6 dBm∼13 dBm.

DISTINGUISHING SENSOR FAULTS FROM SYSTEM FAULTS BY UTILIZING MINIMUM SENSOR REDUNDANCY

2017 ◽  
Vol 41 (3) ◽  
pp. 469-487
Author(s):  
Morteza Taiebat ◽  
Farrokh Sassani
Keyword(s):  
Fault Detection ◽  
Minimum Degree ◽  
A Priori ◽  
Fault Detection And Diagnosis ◽  
A Priori Knowledge ◽  
Sensor Faults ◽  
Control Valves ◽  
Monitoring Process ◽  
Detection And Diagnosis ◽  
And Control

Automated Fault Detection and Diagnosis (FDD) systems depend entirely on the reliability of sensor readings. This paper fills an important gap in the literature by pinpointing the distinction between sensor faults and system faults in the monitoring process. The proposed methodology determines the minimum degree of sensor redundancy necessary to achieve this. A priori knowledge of physical relationships between monitored variables is used to check the credibility of sensor observations. The generalization reveals that for serially connected systems if the number of sensors is greater than 1.5 times of the number of monitored variables, the task of distinguishing between sensor and system faults can be accomplished with certainty, as long as serial causality is valid between the monitored variables. This is verified using a system of interconnected multi reservoirs and control valves.

Design of a fault detection and diagnose system for intelligent unmanned aerial vehicle navigation system

2018 ◽  
Vol 233 (6) ◽  
pp. 2170-2176 ◽  
Author(s):  
Qian Zhang ◽  
Xueyun Wang ◽  
Xiao Xiao ◽  
Chaoying Pei
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fault Detection ◽  
Unmanned Aerial Vehicles ◽  
Navigation System ◽  
Fuzzy System ◽  
Fault Detection And Diagnosis ◽  
Sensor Faults ◽  
Aerial Vehicles ◽  
Detection And Diagnosis

A secure control system is of great importance for unmanned aerial vehicles, especially in the condition of fault data injection. As the source of the feedback control system, the Inertial navigation system/Global position system (INS/GPS) is the premise of flight control system security. However, unmanned aerial vehicles have the requirement of lightweight and low cost for airborne equipment, which makes redundant device object unrealistic. Therefore, the method of fault detection and diagnosis is desperately needed. In this paper, a fault detection and diagnosis method based on fuzzy system and neural network is proposed. Fuzzy system does not depend on the mathematical model of the process, which overcomes the difficulties in obtaining the accurate model of unmanned aerial vehicles. Neural network has a strong self-learning ability, which could be used to optimize the membership function of fuzzy system. This paper is structured as follows: first, a Kalman filter observer is introduced to calculate the residual sequences caused by different sensor faults. Then, the sequences are transmitted to the fault detection and diagnosis system and fault type can be obtained. The proposed fault detection and diagnosis algorithm was implemented and evaluated with real datasets, and the results demonstrate that the proposed method can detect the sensor faults successfully with high levels of accuracy and efficiency.

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