Characterization of Solenoid On-Off Valve Faults: A Faster Analytical Modeling Approach

2021 ◽  
Author(s):  
Hao Tian ◽  
Sichen Li ◽  
Jianbo Liu ◽  
Jiaoyi Hou ◽  
Yongjun Gong
Keyword(s):  
Fault Diagnosis ◽  
Power Systems ◽  
Experimental System ◽  
Solenoid Valve ◽  
Fault Isolation ◽  
Sensor Data ◽  
Analytical Models ◽  
Lumped Mass ◽  
Valve Body ◽  
Computationally Intensive

Abstract Solenoid valves are enabling components for flow and motion control in fluid power systems. It is important for reduction of a machinery’s “off” time if quick fault diagnosis of solenoid valves can be performed on site. Traditional fault diagnosis usually involves signal convolution, machine learning, or fuzzy logics, which can achieve over 90% accuracy but are all computationally intensive, and may be difficult to achieve rapid analysis on site with portable electronics. Among various kinds of faults of solenoid valves, the most common ones are the leakage caused by poor sealing between the poppet and the sleeve, stiction caused by foreign matters in the valve body, or solenoid failure. This paper proposed to simplify the fault diagnosis process by derivation of analytical models of fault characteristics of solenoid on-off valve based on electromagnetics and lumped mass fluid mechanics. Firstly, according to the structure of the solenoid valve, the electromagnetic model of the armature, solenoid, and the air gap is deduced. Secondly, five features from two sensor data are constructed. Fault isolation matrix, tested features are also defined. Thirdly, experimental system was setup to acquire the key threshold values for membership function definition. Finally, two validation tests were conducted and the initial results showed that the proposed method is capable of detecting solenoid valve stiction of various degrees.

Study on Fault Diagnosis of Planetary Gearbox in Unmanned Aerial Vehicle Using Multi sensor Data

2020 ◽  
Vol 20 (4) ◽  
pp. 332-342
Author(s):  
Hyung Jun Park ◽  
Seong Hee Cho ◽  
Kyung-Hwan Jang ◽  
Jin-Woon Seol ◽  
Byung-Gi Kwon ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Unmanned Aerial Vehicle ◽  
Sensor Data ◽  
Planetary Gearbox ◽  
Aerial Vehicle

scholarly journals Fault Diagnosis of Brake Train Based on Multi-Sensor Data Fusion

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4370
Author(s):  
Yongze Jin ◽  
Guo Xie ◽  
Yankai Li ◽  
Xiaohui Zhang ◽  
Ning Han ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Expectation Maximization ◽  
Monitoring Data ◽  
Sensor Data ◽  
Operating Environment ◽  
Mass Model ◽  
Multi Sensor Data Fusion ◽  
Diagnosis Method ◽  
Single Mass ◽  
Parameter Identifications

In this paper, a fault diagnosis method is proposed based on multi-sensor fusion information for a single fault and composite fault of train braking systems. Firstly, the single mass model of the train brake is established based on operating environment. Then, the pre-allocation and linear-weighted summation criterion are proposed to fuse the monitoring data. Finally, based on the improved expectation maximization, the braking modes and braking parameters are identified, and the braking faults are diagnosed in real time. The simulation results show that the braking parameters of systems can be effectively identified, and the braking faults can be diagnosed accurately based on the identification results. Even if the monitoring data are missing or abnormal, compared with the maximum fusion, the accuracies of parameter identifications and fault diagnoses can still meet the needs of the actual systems, and the effectiveness and robustness of the method can be verified.

FAULT DIAGNOSIS IN A PROCESS EXPERIMENTAL CONTROL SYSTEM WITH UNKNOWN FACTORS

2008 ◽  
Vol 07 (01) ◽  
pp. 151-155 ◽  
Author(s):  
AKIRA INOUE ◽  
MINGCONG DENG
Keyword(s):  
Control System ◽  
Fault Diagnosis ◽  
Experimental System ◽  
Detection Problem ◽  
Experimental Control ◽  
Blind System Identification ◽  
Experimental Process ◽  
Identification Approach ◽  
Simulation Results ◽  
Detecting Method

A fault detection problem in a process control experimental system with unknown factors is presented in this paper. The fault detecting method is based on blind system identification approach. The experimental system actuator output includes unknown dynamics and unknown fault signal. By using the fault detecting method, the fault signal is detected. Simulation results for the experimental process are presented to show the effectiveness.

A class of hybrid intelligent system for fault diagnosis in electric power systems

1998 ◽  
Vol 23 (1-3) ◽  
pp. 207-224 ◽  
Author(s):  
Patricia R.S Jota ◽  
Syed M Islam ◽  
Tony Wu ◽  
Gerard Ledwich
Keyword(s):  
Fault Diagnosis ◽  
Power Systems ◽  
Electric Power ◽  
Intelligent System ◽  
Electric Power Systems ◽  
Hybrid Intelligent System

scholarly journals Estimation and Fault Diagnosis of Lithium-Ion Batteries: A Fractional-Order System Approach

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Shulan Kong ◽  
Mehrdad Saif ◽  
Guozeng Cui
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Fractional Order ◽  
Sliding Mode ◽  
Estimation Error ◽  
Lithium Ion ◽  
Fault Isolation ◽  
Sliding Mode Observer ◽  
Fault Detection And Isolation ◽  
Order System

This study investigates estimation and fault diagnosis of fractional-order Lithium-ion battery system. Two simple and common types of observers are designed to address the design of fault diagnosis and estimation for the fractional-order systems. Fractional-order Luenberger observers are employed to generate residuals which are then used to investigate the feasibility of model based fault detection and isolation. Once a fault is detected and isolated, a fractional-order sliding mode observer is constructed to provide an estimate of the isolated fault. The paper presents some theoretical results for designing stable observers and fault estimators. In particular, the notion of stability in the sense of Mittag-Leffler is first introduced to discuss the state estimation error dynamics. Overall, the design of the Luenberger observer as well as the sliding mode observer can accomplish fault detection, fault isolation, and estimation. The effectiveness of the proposed strategy on a three-cell battery string system is demonstrated.

scholarly journals A Novel Fast Training Method for SVM and Its Application in Fault Diagnosis of Service Robot

2015 ◽  
Vol 11 (6) ◽  
pp. 4 ◽  
Author(s):  
Xianfeng Yuan ◽  
Mumin Song ◽  
Fengyu Zhou ◽  
Yugang Wang ◽  
Zhumin Chen
Keyword(s):  
Fault Diagnosis ◽  
Classification Accuracy ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Service Robot ◽  
Support Vector ◽  
Svm Classifier ◽  
Processing Unit ◽  
Training Method ◽  
Fast Training

Support Vector Machines (SVM) is a set of popular machine learning algorithms which have been successfully applied in diverse aspects, but for large training data sets the processing time and computational costs are prohibitive. This paper presents a novel fast training method for SVM, which is applied in the fault diagnosis of service robot. Firstly, sensor data are sampled under different running conditions of the robot and those samples are divided as training sets and testing sets. Secondly, the sampled data are preprocessed and the principal component analysis (PCA) model is established for fault feature extraction. Thirdly, the feature vectors are used to train the SVM classifier, which achieves the fault diagnosis of the robot. To speed up the training process of SVM, on the one hand, sample reduction is done using the proposed support vectors selection (SVS) algorithm, which can ensure good classification accuracy and generalization capability. On the other hand, we take advantage of the excellent parallel computing abilities of Graphics Processing Unit (GPU) to pre-calculate the kernel matrix, which avoids the recalculation during the cross validation process. Experimental results illustrate that the proposed method can significantly reduce the training time without decreasing the classification accuracy.

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