scholarly journals Multi-Fault Diagnosis Approach Based on Updated Interacting Multiple Model for Aviation Hydraulic Actuator

Information ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 410
Author(s):  
Xiaozhe Sun ◽  
Xingjian Wang ◽  
Siru Lin
Keyword(s):  
Fault Diagnosis ◽  
Flight Control ◽  
Failure Modes ◽  
Physical Parameters ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Hydraulic Actuator ◽  
Multiple Faults ◽  
Actuation System ◽  
Diagnosis Method

The aviation hydraulic actuator (HA) is a key component of the flight control system in an aircraft. It is necessary to consider the occurrence of multiple faults under harsh conditions during a flight. This study designs a multi-fault diagnosis method based on the updated interacting multiple model (UIMM). The correspondence between the failure modes and the key physical parameters of HA is found by analyzing the fault mode and mechanism. The key physical parameters of HA can be estimated by employing a series of extended Kalman filters (EKF) related to the different modes of HA. The models in UIMM are updated once the fault is determined. UIMM can reduce the number of fault models and avoid combinatorial explosion in the case of multiple faults. Simulation results indicate that the multi-fault diagnosis method based on UIMM is effective for multi-fault diagnosis of electro-hydraulic servo actuation system.

scholarly journals An IMM-GLR Approach for Marine Gas Turbine Gas Path Fault Diagnosis

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Qingcai Yang ◽  
Shuying Li ◽  
Yunpeng Cao
Keyword(s):  
Gas Turbine ◽  
Estimation Algorithm ◽  
Health Condition ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Multiple Faults ◽  
Fault Severity ◽  
Model Set ◽  
Gas Path Fault ◽  
Update Strategy

An IMM-GLR approach based on interacting multiple model (IMM) and generalized likelihood ratio (GLR) estimation was developed to detect, isolate, and estimate gas turbine gas path fault (including abrupt fault and multiple faults) in the underdetermine estimation conditions. In this approach, a model set representing gas turbine health condition and different fault condition was established, and a corresponding bank of filters was designed. An IMM-based FDI algorithm based on these filters is applied to detect and isolate fault, and a GLR estimation algorithm is used to estimate the fault severity. Then a model set update strategy based on the diagnosed fault was proposed to enable the diagnosis of multiple faults. Several simulation case studies on a marine gas turbine were conducted, and the results show that the IMM-GLR approach not only accurately diagnoses the abrupt gas path fault and multiple gas path faults but also accurately estimates the severity of the detected fault in the underdetermine estimation conditions.

The Faults Detection Methods for Embedded Equipment

2014 ◽  
Vol 602-605 ◽  
pp. 2041-2043
Author(s):  
Bin Wang
Keyword(s):  
Fault Diagnosis ◽  
Failure Modes ◽  
Simulation Models ◽  
Classification Model ◽  
Detection Methods ◽  
Control Procedure ◽  
Element Analysis ◽  
Embedded Control ◽  
Diagnosis Method ◽  
Simulation Results

Due to the objects in the embedded control procedure are difficult to obtain a variety of fault data and fault features, it’s necessary to establish simulation models in accordance with the operational mechanisms of the embedded equipment to simulate and diagnose the practical faults. This paper proposes a SVM integrated diagnostic method and further proposes the faults classification model with improved neural network. The faults diagnose performance is greatly improved by analyzing the types of the faults in different facets. For the embedded valve failure modes, the simulation results of the proposed method are compared with that of the previous mature independent element analysis method. The simulation results show that the fault diagnosis method in this paper can effectively improve the speed and accuracy of fault diagnosis for the embedded equipment.

A hybrid approach for aircraft fault diagnosis based on fault inference and fault identification

2014 ◽  
Vol 118 (1199) ◽  
pp. 81-97 ◽  
Author(s):  
X. Liu ◽  
Z. Liu
Keyword(s):  
Fault Diagnosis ◽  
Flight Control ◽  
Correct Diagnosis ◽  
Hybrid Approach ◽  
Fault Identification ◽  
Multiple Model ◽  
Basic Fault ◽  
Cause Of Failure ◽  
Fault Mode ◽  
Fault Inference

Abstract A cockpit instrumentation system provides various elements of information for pilots. However, logical inference based on a cockpit instruments fault tree (FT) and reliability sometimes cannot give a correct diagnosis of failures. In addition, in flight control systems (FCS), a fault identification method based on the multiple-model (MM) estimator cannot find the basic fault cause. To deal with these problems, a hybrid approach which is capable of integrating inference and fault identification is proposed. In this approach, the event nodes of the FT which have correlations to the FCS are separated into modules. Each module corresponds to a fault mode of the FCS. To use these correlations, fault inference and the MM estimator can share fault diagnosis information. Simulation results show that the proposed approach is helpful in detecting the root cause of failure and is more correct than single fault diagnosis method.

scholarly journals A method for bearing fault diagnosis of mine hoist using convolutional attention autoencoder

2021 ◽  
Author(s):  
Jie Zhang ◽  
Ke Yang ◽  
Yuanyuan Jiang ◽  
Ling Xia
Keyword(s):  
Fault Diagnosis ◽  
Input Signal ◽  
Experimental Validation ◽  
Failure Modes ◽  
Noisy Environment ◽  
Bearing Fault Diagnosis ◽  
Fault Features ◽  
Diagnosis Method ◽  
Actual Operation ◽  
Mine Hoist

Abstract In view of the complex environment and frequent faults in the actual operation of mine hoist, a fault diagnosis method based on Convolution Attention Autoencoder (CAAE) is proposed through theoretical analysis and experimental verification to improve the diagnostic stability of mine hoist under strong noise. First, a CAAE is constructed, which uses a combination of a convolutional neural network (CNN) and a channel attention module (CAM) to compress and encode the input signal, and then the input signal is reconstructed by a decoder to train the CAAE to extract the original signal fault features. Then, a fault diagnosis classifier is constructed to classify different fault patterns. Finally, experimental validation is performed with the Case Western Reserve University bearing dataset. The results show that the method has a strong feature extraction capability and a high classification accuracy for bearing failure modes compared with existing methods. And the experiments on the application effect of the proposed method in noisy environment are conducted to verify that the method is highly effective and challenging.

Fault Diagnosis and Fault-Tolerant Control of a Joystick-Controlled Wheelchair

2008 ◽  
Vol 20 (6) ◽  
pp. 903-911
Author(s):  
Masafumi Hashimoto ◽  
◽  
Yuuki Nakamura ◽  
Kazuhiko Takahashi ◽  
Keyword(s):  
Decision Making ◽  
Fault Diagnosis ◽  
Fault Tolerant ◽  
Fault Tolerant Control ◽  
Experimental Results ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Sensors And Actuators ◽  
Powered Wheelchair ◽  
Decision Making Rule

This paper presents a method of fault diagnosis and fault-tolerant control for a nonholonomic powered wheelchair. Hard faults of sensors and actuators in two drive/steering units of the wheelchair are handled. The fault diagnosis is based on the interacting multiple-model (IMM) estimator. In order to improve fault decisions, we implement mode probability averaging and heuristic decision-making rule in the IMM-based algorithm. A fault-tolerant controller designed based on Ackerman geometry enables safe motion of the wheelchair even if sensors and actuators have partially failed. Experimental results verify the proposed method.

Sensor Fault Detection and Diagnosis in Dead Reckoning System of Mobile Robot: Interacting Multiple-Model Approach

2002 ◽  
Vol 14 (4) ◽  
pp. 342-348
Author(s):  
Masafumi Hashimoto ◽  
◽  
Hiroyuki Kawashima ◽  
Fuminori Oba ◽  
Keyword(s):  
Fault Detection ◽  
Mobile Robot ◽  
Failure Modes ◽  
Dead Reckoning ◽  
Fault Detection And Diagnosis ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Sensor Fault ◽  
Sensor Fault Detection ◽  
Detection And Diagnosis

An interacting multiple-model (IMM) approach to sensor fault detection and diagnosis (FDD) in dead reckoning is proposed for navigating mobile robots. In this approach, changes of sensor normal/failure modes are explicitly modeled as switching from one mode to another in a probabilistic manner, and the sensor FDD and state estimate are achieved via a bank of parallel Kalman filters. To provide better FDD performance, mode probability averaging and heuristic decisionmaking logic are combined with the IMM based FDD algorithm. The proposed FDD is implemented on a skid-steered mobile robot, where 32 system modes (one normal mode and 31 hard sensor failure modes) of 5 sensors (4 wheel-encoders and one yaw-rate gyro) are handled. Experimental results validate the effectiveness of the proposed FDD.

scholarly journals A Dynamic Integrated Fault Diagnosis Method for Power Transformers

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Wensheng Gao ◽  
Cuifen Bai ◽  
Tong Liu
Keyword(s):  
Fault Diagnosis ◽  
Working Conditions ◽  
Failure Modes ◽  
Selection Process ◽  
Power Transformers ◽  
Diagnosis Method ◽  
One Step ◽  
Diagnosis Model ◽  
Transformer Fault ◽  
Dynamic Fault Diagnosis

In order to diagnose transformer fault efficiently and accurately, a dynamic integrated fault diagnosis method based on Bayesian network is proposed in this paper. First, an integrated fault diagnosis model is established based on the causal relationship among abnormal working conditions, failure modes, and failure symptoms of transformers, aimed at obtaining the most possible failure mode. And then considering the evidence input into the diagnosis model is gradually acquired and the fault diagnosis process in reality is multistep, a dynamic fault diagnosis mechanism is proposed based on the integrated fault diagnosis model. Different from the existing one-step diagnosis mechanism, it includes a multistep evidence-selection process, which gives the most effective diagnostic test to be performed in next step. Therefore, it can reduce unnecessary diagnostic tests and improve the accuracy and efficiency of diagnosis. Finally, the dynamic integrated fault diagnosis method is applied to actual cases, and the validity of this method is verified.

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