Sensor Fault Diagnosis of Electro-Mechanical Brake System

2008 ◽  
Author(s):  
Woohyun Hwang ◽  
Kwangjin Han ◽  
Kunsoo Huh ◽  
Jongki Kim ◽  
Joogon Kim ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Intelligent Vehicles ◽  
Fault Detection And Isolation ◽  
Sensor Fault ◽  
Diagnosis System ◽  
Hydraulic Brake ◽  
Model Based ◽  
Proposed Model ◽  
Analytical Redundancy ◽  
Fault Diagnosis System

The brake-by-wire units such as EMB (Electro-Mechanical Brake) will be applied to the intelligent vehicles because the brake-by-wire units are lighter in weights and have faster response compared to conventional hydraulic brake units. However, the brake-by-wire units such as EMB should be at least as reliable as the conventional hydraulic brake units. Because there are no mechanical links between the brake pedal and brake-by-wire actuators, FDI (Fault detection and isolation) is essential in implementing EMB units. In this study, a model-based fault diagnosis system is developed for monitoring the brake status utilizing the analytical redundancy method. The performance of the proposed model-based fault diagnosis system is verified in simulations in various faulty cases.

A Model-Based Fault Diagnosis System for Electro-Hydraulic Brake

2008 ◽  
Author(s):  
Kunsoo Huh ◽  
Kwangjin Han ◽  
Daegun Hong ◽  
Joogon Kim ◽  
Hyungjin Kang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Diagnosis System ◽  
Hydraulic Brake ◽  
Model Based ◽  
Fault Diagnosis System

Probabilistic Model-Based Fault Diagnosis of the Rotor System

2007 ◽  
Author(s):  
Jiye Shao ◽  
Rixin Wang ◽  
Jingbo Gao ◽  
Minqiang Xu
Keyword(s):  
Fault Diagnosis ◽  
Bayesian Network ◽  
Network Model ◽  
Rotating Machinery ◽  
Rotor System ◽  
Bayesian Network Model ◽  
Diagnosis System ◽  
Model Based ◽  
Core Components ◽  
Fault Diagnosis System

The rotor is one of the most core components of the rotating machinery and its working states directly influence the working states of the whole rotating machinery. There exists much uncertainty in the field of fault diagnosis in the rotor system. This paper analyses the familiar faults of the rotor system and the corresponding faulty symptoms, then establishes the rotor’s Bayesian network model based on above information. A fault diagnosis system based on the Bayesian network model is developed. Using this model, the conditional probability of the fault happening is computed when the observation of the rotor is presented. Thus, the fault reason can be determined by these probabilities. The diagnosis system developed is used to diagnose the actual three faults of the rotor of the rotating machinery and the results prove the efficiency of the method proposed.

Reducing the Impact of False Alarms in Induction Motor Fault Diagnosis

2003 ◽  
Vol 125 (1) ◽  
pp. 80-95 ◽  
Author(s):  
Kyusung Kim ◽  
Alexander G. Parlos
Keyword(s):  
Fault Diagnosis ◽  
Induction Motors ◽  
Wavelet Packet ◽  
New Technology ◽  
False Alarms ◽  
Motor Speed ◽  
Diagnosis System ◽  
Model Based ◽  
Fault Diagnosis System ◽  
The Impact

Early detection and diagnosis of incipient faults is desirable for on-line condition assessment, product quality assurance, and improved operational efficiency of induction motors. At the same time, reducing the probability of false alarms increases the confidence of equipment owners in this new technology. In this paper, a model-based fault diagnosis system recently proposed by the authors for induction motors is experimentally compared for fault detection and false alarm performance with a more traditional signal-based motor fault estimator. In addition to the nameplate information required for the initial set-up, the proposed model-based fault diagnosis system uses measured motor terminal currents and voltages, and motor speed. The motor model embedded in the diagnosis system is empirically obtained using dynamic recurrent neural networks, and the resulting residuals are processed using wavelet packet decomposition. The effectiveness of the model-based diagnosis system in detecting the most widely encountered motor electrical and mechanical faults, while minimizing the impact of false alarms resulting from power supply and load variations, is demonstrated through extensive testing with staged motor faults. The model-based fault diagnosis system is scalable to motors of different power ratings and it has been successfully tested with fault data from 2.2kW,373kW, and 597kW induction motors.

Sensor fault diagnosis of gas turbine engines using an integrated scheme based on improved least squares support vector regression

2019 ◽  
Vol 234 (3) ◽  
pp. 607-623
Author(s):  
Yu Hu ◽  
Jietang Zhu ◽  
Zhensheng Sun ◽  
Lijia Gao
Keyword(s):  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Support Vector Regression ◽  
Gas Turbine ◽  
Turbine Engines ◽  
Support Vector ◽  
Gas Turbine Engines ◽  
Sensor Fault ◽  
Diagnosis System ◽  
Fault Diagnosis System

As the flight envelope is widening continuously and operational capability is improving sequentially, gas turbine engines are faced with new challenges of increased operation and maintenance requirements for efficiency, reliability, and safety. The measures for security and safety and the need for reducing the life cycle cost make it necessary to develop more accurate and efficient monitoring and diagnostic schemes for the health management of gas turbine components. Sensors along the gas path are one of the components in gas turbines that play a crucial role in turbofan engines owing to their safety criticality. Failures in sensor measurements often result in serious problems affecting flight safety and performance. Therefore, this study aims to develop an online diagnosis system for gas path sensor faults in a turbofan engine. The fault diagnosis system is designed and implemented using a genetic algorithm optimized recursive reduced least squares support vector regression algorithm. This method uses a reduction technique and recursion strategy to obtain a better generalization performance and sparseness, and exploits an improved genetic algorithm to choose the optimal model parameters for improving the training precision. The effectiveness of the sensor fault diagnosis system is then validated through typical fault modes of single and dual sensors.

scholarly journals Detecting and Learning Unknown Fault States by Automatically Finding the Optimal Number of Clusters for Online Bearing Fault Diagnosis

2019 ◽  
Vol 9 (11) ◽  
pp. 2326 ◽  
Author(s):  
Md Rashedul Islam ◽  
Young-Hun Kim ◽  
Jae-Young Kim ◽  
Jong-Myon Kim
Keyword(s):  
Fault Diagnosis ◽  
Evaluation Method ◽  
Nearest Neighbor ◽  
Optimal Number ◽  
Distribution Factor ◽  
K Nearest Neighbor ◽  
Diagnosis System ◽  
Cluster Evaluation ◽  
Proposed Model ◽  
Fault Diagnosis System

This paper proposes an online fault diagnosis system for bearings that detect emerging fault modes and then updates the diagnostic system knowledge (DSK) to incorporate information about the newly detected fault modes. New fault modes are detected using k-means clustering along with a new cluster evaluation method, i.e., multivariate probability density function’s cluster distribution factor (MPDFCDF). In this proposed model, a heterogeneous pool of features is constructed from the signal. A hybrid feature selection model is adopted for selecting optimal feature for learning the model with existing fault mode. The proposed online fault diagnosis system detects new fault modes from unknown signals using k-means clustering with the help of proposed MPDFCDF cluster evaluation method. The DSK is updated whenever new fault modes are detected and updated DSK is used to classify faults using the k-nearest neighbor (k-NN) classifier. The proposed model is evaluated using acoustic emission signals acquired from low-speed rolling element bearings with different fault modes and severities under different rotational speeds. Experimental results present that the MPDFCDF cluster evaluation method can detect the optimal number of fault clusters, and the proposed online diagnosis model can detect newly emerged faults and update the DSK effectively, which improves the diagnosis performance in terms of the average classification performance.

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