FAULT DIAGNOSIS OF AN INDUSTRIAL MACHINE THROUGH SENSOR FUSION

2008 ◽  
Vol 05 (02) ◽  
pp. 93-110 ◽  
Author(s):  
HAOXIANG LANG ◽  
CLARENCE W. DE SILVA
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Sensor Fusion ◽  
High Reliability ◽  
Ccd Camera ◽  
Fault Detection And Diagnosis ◽  
Frequency Spectra ◽  
Feature Vectors ◽  
Cutting Machine ◽  
Neuro Fuzzy

In this paper, a four layer neuro-fuzzy architecture of multi-sensor fusion is developed for a fault diagnosis system which is applied to an industrial fish cutting machine. An important characteristic of the fault diagnosis approach developed in this paper is to make an accurate decision of the machine condition by fusing information acquired from three types of sensors: Accelerometer, microphone and charge-coupled device (CCD) camera. Feature vectors for vibration and sound signals from their fast Fourier transform (FFT) frequency spectra are defined and extracted from the acquired information. A feature-based vision method is applied for object tracking in the machine, to detect and track the fish moving on the conveyor. A four-layer neural network including a fuzzy hidden layer is developed in the paper to analyze and diagnose existing faults. Feature vectors of vibration, sound and vision are provided as inputs to the neuro-fuzzy network for fault detection and diagnosis. By proper training of the neural network using data samples for typical faults, six crucial faults in the fish cutting machine are detected with high reliability and robustness. On this basis, not only the condition of the machine can be determined for possible retuning and maintenance, but also alarms to warn about impending faults may be generated during the machine operation.

Fault Diagnosis of an Industrial Machine Through Neuro-Fuzzy Sensor Fusion

2007 ◽  
Author(s):  
Haoxiang Lang ◽  
Ying Wang ◽  
Clarence W. de Silva
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Sensor Fusion ◽  
Scale Invariant ◽  
Frequency Spectra ◽  
Diagnosis System ◽  
Neuro Fuzzy ◽  
Fault Diagnosis System ◽  
Industrial Machine ◽  
Sound Vibration

In this paper a neuro-fuzzy approach of multi-sensor fusion is developed for a fault diagnosis system. The approach is validated by applying it to a machine called the Iron Butcher, which is used in industry for the removal of heads in fish prior to further processing for canning. An important goal of this approach developed in this paper is to make an accurate decision of the machine condition by fusing information from different sensors. Specifically, sound, vibration and vision measurements are acquired from the machine using a microphone, an accelerometer and a digital CCD camera, respectively. Next, the sound and vibration signals are transformed into the frequency domain using Fast Fourier Transform (FFT). A feature vector from the FFT frequency spectra is defined and extracted from the acquired information. Also, a feature based vision tracking approach—the Scale Invariant Feature Transform (SIFT)—is applied to the vision data to track the object of interest (fish) in a robust manner. In the diagnosis process, a candidate fish is detected and tracked. Sound, vibration and vision features are extracted as inputs for the neuro-fuzzy fault diagnosis system. A four-layer neural network including a fuzzy hidden layer is developed to analyze and diagnose any existing faults. By training the neural network with sample data for typical faults, six crucial faults in the fish cutting machine are detected precisely. In this manner, alarms to warn about impending faults may be generated as well during the machine operation. Developed approaches are validated using computer simulations and physical experimentation using the industrial machine.

Diagnosis of process faults with neural networks and principal component analysis

2000 ◽  
Vol 214 (2) ◽  
pp. 131-143 ◽  
Author(s):  
J B Gomm ◽  
M Weerasinghe ◽  
D Williams
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Principal Component Analysis ◽  
Fault Diagnosis ◽  
Principal Component ◽  
Process Variable ◽  
Component Analysis ◽  
Fault Detection And Diagnosis ◽  
Processing Plant ◽  
Large Network

Industrial plants often have many process variable measurements available, which can be monitored for fault detection and diagnosis. Using all these variables as inputs to an artificial neural network for fault diagnosis can result in an impractically large network, with consequent long training times and high computational requirement during use. Principal component analysis (PCA) is investigated in this paper for generating a reduced number of variables to be used as neural network inputs for fault diagnosis. The main application described is to a real industrial nuclear fuel processing plant. A simulated chemical process was also used to assist the development of the techniques. Results in both applications demonstrate satisfactory fault diagnosis performance with a reduction in the number of neural network parameters of approximately 50 per cent using PCA. The paper also includes some introductory material on PCA and neural networks, and their application to process fault diagnosis.

scholarly journals Gear Fault Diagnosis Based on VMD Sample Entropy and Discrete Hopfield Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Jiakai Ding ◽  
Dongming Xiao ◽  
Liangpei Huang ◽  
Xuejun Li
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Hopfield Neural Network ◽  
Sample Entropy ◽  
Feature Vectors ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Diagnosis Method ◽  
Gear Fault Diagnosis

The gear fault signal has some defects such as nonstationary nonlinearity. In order to increase the operating life of the gear, the gear operation is monitored. A gear fault diagnosis method based on variational mode decomposition (VMD) sample entropy and discrete Hopfield neural network (DHNN) is proposed. Firstly, the optimal VMD decomposition number is selected by the instantaneous frequency mean value. Then, the sample entropy value of each intrinsic mode function (IMF) is extracted to form the gear feature vectors. The gear feature vectors are coded and used as the memory prototype and memory starting point of DHNN, respectively. Finally, the coding vector is input into DHNN to realize fault pattern recognition. The newly defined coding rules have a significant impact on the accuracy of gear fault diagnosis. Driven by self-associative memory, the coding of gear fault is accurately classified by DHNN. The superiority of the VMD-DHNN method in gear fault diagnosis is verified by comparing with an advanced signal processing algorithm. The results show that the accuracy based on VMD sample entropy and DHNN is 91.67% of the gear fault diagnosis method. The experimental results show that the VMD method is better than the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and empirical mode decomposition (EMD), and the effect of it in the diagnosis of gear fault diagnosis is emphasized.

Fault Diagnosis System of the Fire Control System Based on Fuzzy Neural Network

2012 ◽  
Vol 605-607 ◽  
pp. 828-831
Author(s):  
Peng Jun Zhang ◽  
Yu Cheng Bo ◽  
Hui Yuan Wang ◽  
Qiang Li
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fault Diagnosis ◽  
Structure Design ◽  
Fault Detection And Diagnosis ◽  
State Monitoring ◽  
Fire Control ◽  
Fault Recognition ◽  
Fuzzy Neural ◽  
Diagnosis Rate

the paper mainly solves the questions of real time fault detection and diagnosis for fire control system. Function structure and fault characteristics of the fire control system be hierarchical analyzed, through fault omen character and fault reason make up of fault stylebook, use neural network training to improve the fault diagnosis accuracy, select implication operator and calculate the fuzzy membership degree matrix of fault information, through hierarchical reasoning to complete fault recognition and confirm fault source to improve the diagnosis rate. The system is multifunctional which includes of state monitoring, fault diagnosis, diagnosis opinion, the distributed principal and subordinate structure design is propitious to improve reliability and adaptability of the system.

scholarly journals Development of a Methodology Using Artificial Neural Network in the Detection and Diagnosis of Faults for Pneumatic Control Valves

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 853
Author(s):  
Ana Andrade ◽  
Kennedy Lopes ◽  
Bernardo Lima ◽  
André Maitelli
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Decision Tree ◽  
Control Systems ◽  
Market Competition ◽  
Fault Detection And Diagnosis ◽  
Industrial Plant ◽  
Computational Time ◽  
Control Valves ◽  
Detection And Diagnosis

To satisfy the market, competition in the industrial sector aims for productivity and safety in industrial plant control systems. The appearance of a fault can compromise the system’s proper functioning process. Therefore, Fault Detection and Diagnosis (FDD) methods contribute to avoiding any undesired events, as there are techniques and methods that study the detection, isolation, identification and, consequently, fault diagnosis. In this work, a new methodology that uses faults emulation to obtain parameters similar to the Development and Application of Methods for Diagnosis of Actuators in Industrial Control Systems (DAMADICS) benchmark model will be developed. This methodology uses previous information from tests on sensors with and without faults to detect and classify the situation of the plant and, in the presence of faults, perform the diagnosis through a process of elimination in a hierarchical manner. In this way, the definition of residue signature is used as well as the creation of a decision tree. The whole process is carried out incorporating FDD techniques, through the Non-Linear Auto-Regressive Neural Network Model With Exogenous Inputs (NARX), in the diagnosis of the behavioral prediction of the signals to generate the residual values. Then, it is applied to the construction of the decision tree based on the most significant residue of a certain signal, enabling the process of acquisition and formation of the signature matrix. With the procedures in this article, it is possible to demonstrate a practical and systematic method of how to emulate faults for control valves and the possibility of carrying out an analysis of the data to acquire signatures of the fault behavior. Finally, simulations resulting from the most sensitized variables for the production of residuals that is generated by neural networks are presented, which are used to obtain signatures and isolate the flaws. The process proves to be efficient in computational time and makes it easy to present a fault diagnosis strategy that can be reproduced in other processes.

scholarly journals A Simultaneous Fault Diagnosis Method Based on Cohesion Evaluation and Improved BP-MLL for Rotating Machinery

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yixuan Zhang ◽  
Rui Yang ◽  
Mengjie Huang ◽  
Yu Han ◽  
Yiqi Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Error Function ◽  
Diagnostic Algorithm ◽  
High Sensitivity ◽  
Rotating Machinery ◽  
Feature Vectors ◽  
Evaluation Algorithm ◽  
Fault Diagnostic ◽  
Low Dimensional

In this paper, an improved simultaneous fault diagnostic algorithm with cohesion-based feature selection and improved backpropagation multilabel learning (BP-MLL) classification is proposed to localize and diagnose different simultaneous faults on gearbox and bearings in rotating machinery. Cohesion evaluation algorithm selects high sensitivity feature parameters from time and frequency domain in high-dimensional vectors to construct low-dimensional feature vectors. The BP-MLL neural network is utilized for fault diagnosis by classifying the feature vectors. An effective global error function is proposed in BP-MLL neural network by modifying distance function to improve both generalization ability and fault diagnostic ability of full-labeled and nonlabeled situations. To demonstrate the effectiveness of the proposed method, simultaneous fault diagnosis experiments are conducted via wind turbine drivetrain diagnostics simulator (WTDDS). The experiment results show that the proposed method has better overall performance compared with conventional BP-MLL algorithm and some other learning algorithms.

Neural Network Based Expert System for Steel Bar Pipeline Fault Diagnosis

2011 ◽  
Vol 421 ◽  
pp. 590-594 ◽  
Author(s):  
Jian Yin ◽  
Li Ting Mei
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Expert System ◽  
Distributed Processing ◽  
Economic Loss ◽  
Fault Detection And Diagnosis ◽  
Automated System ◽  
Hydraulic Pressure ◽  
Production Pipeline ◽  
Steel Bars

In modern continuing steel bars production pipeline, there are various equipments and automatic controls combining with mechanism, electricity, hydraulic pressure and aerodynamic. This field of fault detection and diagnosis deals with design of computer-based automated system that can assist plant operators. The neural network based expert system have advantages of parallel distributed processing, high robust, fault tolerance, adaptive and self-organization. Applying neural network based expert system for the condition detection and fault diagnosis of steel bars pipeline can reduce the economic loss caused by system downtime.

scholarly journals Gear Fault Diagnosis Based on Kurtosis Criterion VMD and SOM Neural Network

2019 ◽  
Vol 9 (24) ◽  
pp. 5424 ◽  
Author(s):  
Dongming Xiao ◽  
Jiakai Ding ◽  
Xuejun Li ◽  
Liangpei Huang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Characteristic Curve ◽  
Vibration Signal ◽  
Good Effect ◽  
Feature Vectors ◽  
Gear Fault ◽  
Diagnosis Method ◽  
Som Neural Network ◽  
Gear Fault Diagnosis

A gear fault diagnosis method based on kurtosis criterion variational mode decomposition (VMD) and self-organizing map (SOM) neural network is proposed. Firstly, the VMD algorithm is used to decompose the gear vibration signal, and the instantaneous frequency mean is calculated as the evaluation index, and the characteristic curve is drawn to screen out the most relevant intrinsic mode functions (IMFs) of the original vibration signal. Then, the number of VMD decompositions is determined, and the kurtosis value of IMFs are extracted to form the feature vectors. Then, the kurtosis value feature vectors of IMFs are normalized to form the kurtosis value normalized vectors. Finally, the normalized vectors of kurtosis value are input into SOM neural network to realize gear fault diagnosis. When the number of training times of SOM neural network is 100, the gear fault category is accurately classified by SOM neural network. The results show that when the training times of SOM neural network is 100 times, the gear fault diagnosis method, based on the kurtosis criterion VMD and SOM neural network is 100%, which indicates that the new method has a good effect on gear fault diagnosis.

scholarly journals Frequency Occurrence Plot-Based Convolutional Neural Network for Motor Fault Diagnosis

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1711
Author(s):  
Eduardo Jr Piedad ◽  
Yu-Tung Chen ◽  
Hong-Chan Chang ◽  
Cheng-Chien Kuo
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Axis Deviation ◽  
Frequency Spectra ◽  
Load Variations ◽  
Vibration Sensors ◽  
New Feature ◽  
Frequency Occurrence

A novel motor fault diagnosis using only motor current signature is developed using a frequency occurrence plot-based convolutional neural network (FOP-CNN). In this study, a healthy motor and four identical motors with synthetically applied fault conditions—bearing axis deviation, stator coil inter-turn short circuiting, a broken rotor strip, and outer bearing ring damage—are tested. A set of 150 three-second sampling stator current signals from each motor fault condition are taken under five artificial coupling loads (0, 25%, 50%, 75% and 100%). The sampling signals are collected and processed into frequency occurrence plots (FOPs) which later serve as CNN inputs. This is done first by transforming the time series signals into its frequency spectra then convert these into two-dimensional FOPs. Fivefold stratified sampling cross-validation is performed. When motor load variations are considered as input labels, FOP-CNN predicts motor fault conditions with a 92.37% classification accuracy. It precisely classifies and recalls bearing axis deviation fault and healthy conditions with 99.92% and 96.13% f-scores, respectively. When motor loading variations are not used as input data labels, FOP-CNN still satisfactorily predicts motor condition with an 80.25% overall accuracy. FOP-CNN serves as a new feature extraction technique for time series input signals such as vibration sensors, thermocouples, and acoustics.

scholarly journals Fault Diagnosis of Electromechanical Actuator Based on VMD Multifractal Detrended Fluctuation Analysis and PNN

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Hongmei Liu ◽  
Jiayao Jing ◽  
Jian Ma
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Detrended Fluctuation Analysis ◽  
Probabilistic Neural Network ◽  
Fluctuation Analysis ◽  
Electromechanical Actuators ◽  
Vibration Signals ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Feature Vectors ◽  
Detrended Fluctuation

Electromechanical actuators (EMAs) are more and more widely used as actuation devices in flight control system of aircrafts and helicopters. The reliability of EMAs is vital because it will cause serious accidents if the malfunction of EMAs occurs, so it is significant to detect and diagnose the fault of EMAs timely. However, EMAs often run under variable conditions in realistic environment, and the vibration signals of EMAs are nonlinear and nonstationary, which make it difficult to effectively achieve fault diagnosis. This paper proposed a fault diagnosis method of electromechanical actuators based on variational mode decomposition (VMD) multifractal detrended fluctuation analysis (MFDFA) and probabilistic neural network (PNN). First, the vibration signals were decomposed by VMD into a number of intrinsic mode functions (IMFs). Second, the multifractal features hidden in IMFs were extracted by using MFDFA, and the generalized Hurst exponents were selected as the feature vectors. Then, the principal component analysis (PCA) was introduced to realize dimension reduction of the extracted feature vectors. Finally, the probabilistic neural network (PNN) was utilized to classify the fault modes. The experimental results show that this method can effectively achieve the fault diagnosis of EMAs even under diffident working conditions. Simultaneously, the diagnosis performance of the proposed method in this paper has an advantage over that of EMD-MFDFA method for feature extraction.

Sign in / Sign up

Export Citation Format

Share Document