machine fault diagnosis

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IWSNs with On-sensor Data Processing for Machine Fault Diagnosis

IWSNs with On-sensor Data Processing for Machine Fault Diagnosis

Abstract: Machine fault diagnosis systems need to collect and transmit dynamic monitoring signals, like vibration and current signals, at high-speed. However, industrial wireless sensor networks (IWSNs) and Industrial Internet of Things (IIoT) are generally based on low-speed wireless protocols, such as ZigBee and IEEE802.15.4. To address this tension when implementing machine fault diagnosis applications in IIoT, this paper proposes a novel IWSN with on-sensor data processing. On-sensor wavelet transforms using four popular mother wavelets are explored for fault feature extraction, while an on-sensor support vector machine classifier is investigated for fault diagnosis. The effectiveness of the presented approach is evaluated by a set of experiments using motor bearing vibration data. The experimental results show that compared with raw data transmission, the proposed on-sensor fault diagnosis method can reduce the payload transmission data by 99.95%, and reduce the node energy consumption by about 10%, while the fault diagnosis accuracy of the proposed approach reaches 98%.

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Machine Fault Diagnosis and Prognosis: The State of The Art

Machine Fault Diagnosis and Prognosis: The State of The Art

In condition monitoring practice, knowledge from domain specific experts is usually inaccurate and reasoning on knowledge is often imprecise. Therefore, measures of the uncertainties in knowledge and reasoning are required for ES to provide more robust problem solving. Unremarkably used uncertainty measures are probability, fuzzy member functions in fuzzy logic theory and belief functions in belief networks theory. An example of applying fuzzy logic to machine fault classification was given in [76] to classify frequency spectra representing various rolling element bearing faults. Du and Yeung [77] introduced an approach, so- called fuzzy transition probability which combined transition probability with the fuzzy set, to monitor progressive faults. Fuzzy logic is also incorporated with other techniques such as neural networks and ES for fault diagnostic application. For example, Zhang et al. [78] developed an FNN for fault diagnosis of rotary machines to improve the recognition rate of pattern recognition, especially in the case that sample data are similar. Lou and Loparo [79] employed an adaptive neural-fuzzy inference system as a diagnostic classifier for bearing fault diagnosis. Liu et al. [80] applied fuzzy logic and ESs to build a fuzzy ES for bearing fault detection. Chang et al. [81] built a system for decision-making support in a power plant using both rule-based ES and fuzzy logic. Genetic algorithms (GAs), which are the most ordinarily used type of EA, have been applied to machine diagnostics. Several examples of ANN incorporating GA and other EAs for machine fault classification and diagnostics are [82-84]. A technique called support vector machine (SVM) is a new general machine learning tool based on the structural risk minimization principle. It has received much consideration in recent times due to its high accuracy and good generalization capabilities. The use of SVM and its extension for machine fault diagnosis were summarized in [85].

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Motor current signal analysis using a modified bispectrum for machine fault diagnosis

Motor current signal analysis using a modified bispectrum for machine fault diagnosis

The analysis of the induction motor current signal with bispectrum clearly has significant potential as a means of non-intrusively detecting the presence of incipient faults in its driven equipment items by extracting the nonlinear interaction of linkage flux due to load variation. However, the conventional bispectrum is not so adequate in representing the current signals with AM features because it cannot include sideband pairs simultaneously and the random variation of sidebands phases. A modified bispectrum i.e. MS bispectrum is then introduced to obtain a more accurate and efficient representation of the current signals. Based on the analysis, a normalised bispectral peak in conjunction with signal kurtosis is developed to diagnose common compressor faults including valve leakage, inter-cooler leakage and belt looseness. The classification results shows that the low feature values can be used to differentiate the belt looseness from other fault cases and valve leakage and inter-cooler leakage can be separated easily using two linear classifiers.

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Advancement of Fault Diagnosis and Detection Process in Industrial Machine Environment | Journal of Engineering Sciences

Advancement of Fault Diagnosis and Detection Process in Industrial Machine Environment | Journal of Engineering Sciences

Abstract. Machine fault diagnosis is a very important topic in industrial systems and deserves further considera- tion in view of the growing complexity and performance requirements of modern machinery. Currently, manufactur- ing companies and researchers are making a great attempt to implement efficient fault diagnosis tools. The signal processing is a key step for the machine condition monitoring in complex industrial rotating electrical machines. A number of signal processing techniques have been reported from last two decades conventionally and effectively ap- plied on different rotating machines. Induction motor is the one of widely used in various industrial applications due to small size, low cost and operation with existing power supply. Faults and failure of the induction machine in indus- try can be the cause of loss of throughput and significant financial losses. As compared with the other faults with the broken rotor bar, it has significant importance because of severity which leads to a serious breakdown of motor. De- tection of rotor failure has become significant fault but difficult task in machine fault diagnosis. The aim of this paper is indented to summarizes the fault diagnosis techniques with the purpose of the broken rotor bar fault detection.

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Computation Effort and Effect of Signal Processing Techniques in Condition Monitoring and Fault Diagnosis of Machine

Computation Effort and Effect of Signal Processing Techniques in Condition Monitoring and Fault Diagnosis of Machine

ABSTRACT: Vibration signal analysis is robust method of incipient fault diagnosis and condition monitoring. Vibration signature carries dynamics information of machine, when the fault developed in the machine some of the dynamics of machine gets change which changes the vibration pattern. The machine fault diagnosis based on continues assessment procedure need effective signal processing and feature extraction technique. A computation effort of signal processing technique plays important role in the real time condition monitoring and fault diagnosis thus an effort is given for finding and verifying the effectiveness of the different signal processing techniques in terms of the computation effort. The robustness of wavelet transform is also discussed in this reseach. The experimental outcomes show that the proposed method can used as effective tool of condition monitoring.

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Implementation of Efficient Artificial Neural Network Data Fusion Classification Technique for Induction Motor Fault Detection | Journal of Engineering Sciences

Implementation of Efficient Artificial Neural Network Data Fusion Classification Technique for Induction Motor Fault Detection | Journal of Engineering Sciences

This paper discussed the machine fault diagnosis using the sensor fusion technique D-S evidence theory. Through the theoretical and practically, we found this theory very efficient and realistic in machine fault diag- nosis concept. A critical comparison is also performed between the different sensors fusion in respect to time which also show the accuracy percentage of D-S. The results shows that it effectively enhance the reliability of machine diagnosis and very much decreased the probabil- ity of uncertainty. It also detects the different faults time- ly to reduce the cause loss of throughput and significant financial losses among the industries. Therefore, it can be believed that the fault diagnosis of electric machines is a significant investigate topic with great potential for appli- cation in industry.

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Polymerizer fault diagnosis algorithm based on improved the GA LMBP

Polymerizer fault diagnosis algorithm based on improved the GA LMBP

Research on fault diagnosis Algorithm for Large polymerization kettle, fault code type is defined. The corresponding polymerizer five state based on LMBP Neural Network output codes are: normal running (0000), Motor failure (0001) , Mechanical seal failure ( 0010 ), gear box failure (0100), machine component failure (1000). By GA-LMBP neural network fault diagnosis tests on polymerization kettle, first select the table that corresponds to 3 of the 10 sets of data, or 80 per cent sampled-data as a test sample, the simulation results as shown in figure 4, train number of steps is 11, the error is 0.00036841, the target error of 10-4.Fault diagnosis in table 5, this time fault diagnosis accuracy rate of 100.

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Analog Circuit Diagnosis Based on SubKPCA SVM

Analog Circuit Diagnosis Based on SubKPCA SVM

Fault diagnosis for analog circuit is still a challenging subject in the circuit test research field. Due to the inherent characteristics of analog circuit [1-2], such as its nonlinearity, continuous response and tolerance on component parameters, etc., inducing the diversity and complexity of fault types of the circuit, it is difficult for the conventional fault diagnosis theories and methods to achieve the expected results in practical engineering. Hence, it is very important to explore some efficient fault diagnosis theories and methods to meet the development of analog circuit.

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Application of Improved Deep Auto Encoder Network in Rolling Bearing Fault Diagnosis

Application of Improved Deep Auto Encoder Network in Rolling Bearing Fault Diagnosis

DOI: 10.4236/jcc.2018.67005 42 Journal of Computer and Communications proposed a multi-layer over-limit learning machine method to learn the fault vi- bration time domain signal and diagnose the fault of the rolling bearing. Liu et al. [7] proposed a method for extracting fault characteristics of rolling bearings based on fault characteristic trend line template. According to the fault characte- ristic trend line, the method finds the bearing fault characteristics and avoids the shortcomings in the order tracking process. A rolling bearing fault feature ex- traction method based on adaptive noise-based complete empirical mode de- composition (CEEMDAN) combined with IMF sample entropy is used to adap- tively decompose the vibration signal [8]. In order to extract the fault characte- ristics of rolling bearings accurately and stably, Liu et al. [9] proposed a feature extraction method based on variational mode decomposition and singular value decomposition. However, the K value in this method needs to be given in ad- vance, and the determination or range of other parameters is still lack of theo- retical basis. The most critical part of the data-driven fault diagnosis method is the extraction of bearing fault characteristics [10]. Due to the increasing number of bearing equipment, the frequency of collecting samples is getting higher and higher, which makes bearing faults fall into massive data problems. The method adopted above requires a large amount of prior knowledge, rich theoretical knowledge of signal processing and practical experience as a support in the process of feature extraction. Moreover, the number of samples selected by the fault feature is small, which cannot adequately reflect the potential information of the bearing fault data, and reduces the accuracy of fault diagnosis. Therefore, it is especially important to choose a suitable method for fault diagnosis of roll- ing bearings.

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Machine Learning based Early Fault Diagnosis of Induction Motor for Electric Vehicle Application

Machine Learning based Early Fault Diagnosis of Induction Motor for Electric Vehicle Application

During the recent few years, due to increase in geo-political awareness, affordable pricings, significant incentives from the government, cheaper cost to run has lead to increase in sales and production of electric vehicles (EVs). It is being predicted that if this current electric car revolution continues, by 2025, EVs will be as cheap as gasoline cars and by 2038, EV sales shall surpass internal combustion engine cars [1]. Although the basic concept of electric vehicle dates back to 1800s, EVs have not been able to cater to the needs of commercial usage like gasoline cars have been. Thus, although the technology is not new, the commercialization of EVs is fairly new and has not reached the maturity of gasoline cars. Thus, the kind of problems that might occur in the long term usage of EVs are at their primitive stages. There are mechanics every where to fix any kind of issues arising in gasoline vehicles. But when it comes to EVs, according to market survey more than 90% of mechanic car repair shops are not ready or have enough capabilities to fix problems arising in an electric motor run vehicle. With an alarming production ramp up of EVs some of which are autonomous at the same time, it is important to understand and prevent any faults that might occur in the powertrain of EVs in future for safety of the passengers. As more EVs are being used commercially, some of these EVs might have a fault occurring due to manufacturing defect, irrational usage, long-term wear and tear or unprecedented circumstances, which are inevitable.

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OPTIMIZATION OF HIGH VOLTAGE POWER SUPPLY FOR INDUSTRIAL MICROWAVE GENERATORS 
FOR ONE MAGNETRON

OPTIMIZATION OF HIGH VOLTAGE POWER SUPPLY FOR INDUSTRIAL MICROWAVE GENERATORS FOR ONE MAGNETRON

Analog circuit plays an important role in electronic circuits and systems. Although most part of an electronic system is digital, about 80% of the faults occur in the analog segment [1]. Fault diagnosis in digital electronic circuits has been successfully developed to the point of automation. As compared with the digital circuits, analog circuits fault diagnosis is known to be difficultly due to the huge number of possible faults, the lack of good fault models for analog components similar to the stuck-at-one and stuck-at-zero fault models, which are widely accepted by the digital test community, the presence of components tolerance and the inherent nonlinearity of the circuits. Even linear circuits exhibit nonlinear relations between circuit parameters and the output response.

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Bearing Fault Diagnosis using Multiclass Support Vector Machine with efficient Feature Selection Methods

Bearing Fault Diagnosis using Multiclass Support Vector Machine with efficient Feature Selection Methods

Consequently an effective signal processing method is of ut- most importance for the extraction of damage sensitive fea- tures during the condition monitoring of bearings. Generally, the failure of a mechanical system is always accompanied with the changes of vibration characteristics from linear or weak nonlinear to strong nonlinear dynamics [4,5].Until now, in the field of bearing fault detection, a variety of approaches and time–frequency signal processing tools have been utilized. Wavelet transform (WT) has been widely used as a de-noising tool as well as for feature extraction in rotating machinery diagnostics [6].The Wavelet Transform (WT) provides power- ful multi-resolution analysis in both time and frequency domains. The fault diagnosis of rolling bearing in early stage using wavelet packet transform and empirical mode decompo- sition were combined and extracted features are given as input to neural network after analysing the shortcomings of current feature extraction and fault diagnosis technologies[7].In past decades, sometime-frequency analysis methods, including short time Fourier transform (STFT) [8], Wigner–Ville distri- bution (WVD) [9,10], and local mean decomposition(LMD) [11], have been formulated to probe non-stationary data and applied to feature extraction of defective rotary machinery. However, each of these methods leaves something to be de- sired and some even perform badly in analysing non- stationary and nonlinear data [12].

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Research on Diagnosis of AC Engine Wear Fault Based on Support Vector Machine and Information Fusion

Research on Diagnosis of AC Engine Wear Fault Based on Support Vector Machine and Information Fusion

Abstract—Support Vector Machine (SVM) and information fusion technology based on D-S evidence theory are used to diagnose wear fault of AC engines. Firstly, based on a number of frequently used oil sample analysis methods for detecting engine wear fault, establish corresponding sub SVM classifier. The classifier can reflect the mapping relation between fault symptoms and fault types and achieve the result for a single diagnosis item. And then, use D-S evidence theory to make information fusion over result for a single diagnosis item so as to make fault diagnosis. With diagnosis of AC engine wear fault serving as example, example testing is performed. The result shows that in comparison with conventional methods, the combination of SVM and information fusion technology is fast and effective, suitable for diagnosis of AC engine wear fault.

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Ontology-based fault diagnosis for power transformers

Ontology-based fault diagnosis for power transformers

Modelling transformer thermal dynamics is regarded as one of the most essential issues and the construction of an accurate thermal model is an im- portant aspect of transformer condition monitoring. The generally accepted methods reported by IEC [5] and IEEE [7], can be used to predict the zones of hot-spot temperature in a transformer as the sum of the ambient tempera- ture, mixed top-oil temperature rise above ambient and hot-spot rise above the mixed top-oil temperature. The two steady-state temperature rises of top-oil and bottom-oil above ambient can be estimated separately. The compari- son between the evaluation of the calculated and the measured temperatures, which refer to the IEC power transformer thermal models, has been discussed in [1][4][10]. The TM is to use the data of Winding Temperature Indicator (WTI), Top-oil Temperature (TOT) and Bottom-oil Temperature (BOT) for transformer fault diagnosis. Table 1.1 shows the rules of the fault diagnosis using TM.

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Application research on rough set  neural network in the fault diagnosis system of ball mill

Application research on rough set neural network in the fault diagnosis system of ball mill

The model used 3 layers BP neural network structure (that is: input layer, hidden layer and output layer), the goal was diagnosis for the ball mill failure. It builded BP neural network model with the fault samples of before and after rough set attribute reduction. Neural network training function was trained , its learning rate was variable in the process of training; learning function was the function based on gradient descent method: learned ; the transfer function was log-sigmoid; the performance function was mean-variance function mse; the training number was 2000; the training error was 0.001; the learning rate was 0.08; the input layer were 15; the output layer were 3; the hidden layer had been selected 31 through the training. The training results of BP neural network before attribute reduction was shown in Fig.2. The fault samples (such as {b, d, f,g, i} ) after rough set attribute reduction built BP neural network model, the training results had been shown in Fig.3.

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Thermal Image Based Fault Diagnosis of Gears using Support Vector Machines

Thermal Image Based Fault Diagnosis of Gears using Support Vector Machines

method of areas selection of image differences (MoASoID) to extract the thermal image-based features for monitoring the three different conditions (i.e. healthy motor, the motor with a defective ring of squirrel-cage and the motor with two broken bars) of induction motor. The proposed MoASoID was used to compare the different training sets together and to choose the regions with the biggest variations for the identification process. Klien et al. [20] proposed a thermal imaging based technique for analyzing and diagnosing the non-stationary time-frequency to RPM order representations (TFRs) of vibration-acoustic data, acquired from healthy and defective bearings. Lim et al. [15] developed an automatic fault diagnostic system based on features extracted from vibration signals and thermal images. SVM was employed to distinguish the machinery faults and it has been found that the success rate achieved with thermal image data is higher as compared to that obtained with vibration data.

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Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

ANN implements RUL estimation and prediction of future health conditions by including direct or indirect observa- tion data, independent from the failure process of a phys- ical model. Various types of data can be used as the input of ANN, such as some process variables, monitoring data (vibration signals), evaluation characteristics (age and stop time), and some historical features. The output of ANN is RUL prediction or performance degradation assessment, which is used for conducting effective maintenance strategies. ANNs widely used in fault prediction include BPNN [91–95], radial basis function network (RBFN), and RNN [96]. Ahmadzadeh, et al [94], proposed a three-layer feedforward BPNN for RUL estimation of grinding mill liners, which considered degeneration and condition mon- itoring data as the inputs of ANN, and used RUL as the output of ANN. Rodriguez, et al [95], presented ANN (six input layers, three hidden layers, and one output layer) to predict and simulate the behavior of life-cycle assessment in blades of steam turbines. In view of the shortcomings of traditional incremental training methods in long-term pre- diction, Malhi, et al [96], proposed an RNN based on competitive learning method to improve the accuracy in long-term prediction of rolling bearings. Mahamad, et al [97], used feedforward neural network and the Levenberg- Marquardt training algorithm to predict the RUL of rolling bearings. Considering the complexity and nonlinearity of the pitch system, and the difficulty of describing with precise mathematical model, Chen, et al [98], proposed ANFIS based on a prior knowledge, which was used to predict wind turbine pitch faults. Existing ANN methods predict the RUL by using failure history data, but sus- pended historical data are rarely utilized. Hong, et al [99], used a self-organizing map, which combined wavelet packet and EMD for feature extraction, to estimate bearing performance degradation. Javed, et al [100], used ELM and fuzzy clustering to predict the degradation state and the RUL of complex nonlinear systems. Compared with ANN, ELM improved the algorithm efficiency by randomly selecting hidden layer parameters. Zhang, et al [101], proposed a multi-objective DBN ensemble method for RUL estimation.

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Power Transformer Fault Diagnosis based on Deep Learning

Power Transformer Fault Diagnosis based on Deep Learning

(3) The research findings show that, to the CDLNN diagnostic method, as the pre-training set increases, the average accuracy rate of fault diagnosis increases constantly. The method is applicable to the training of a large number samples and has a good scalability. Compared with the fault diagnosis methods of BPNN and SVM, its average diagnostic accuracy rate is higher, so that it can provide more accurate reference information for the overhaul of the power transformer.

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Research Review and Prospect of Fault Diagnosis Method of Satellite Power System Based on Machine Learning

Research Review and Prospect of Fault Diagnosis Method of Satellite Power System Based on Machine Learning

This fault diagnosis method mainly conducts fault analysis by establishing the model of system structure and function. First, input data will be given to the model. If there is a certain difference between the actual output result and the output under normal circumstances, system faults will be determined. Because this method can avoid defects in gaining knowledge and it can wide fault coverage and fast processing speed, it is very convenient to be used to establish the system model. During the research process, Shao Jiye [12] analyzed the correlation between the parameters of the satellite power system and each component, established the Bayesian network satellite power model, which realized the fault diagnosis of the satellite power. Song Qijiang [13] proposed a hierarchical directed graph diagnosis method based on fault propagation and symbol digraph. It achieved fault diagnosis of power supply by establishing a qualitative model. Jin Yang [14] combined the diagnostic separation method with the transfer system model to make fault diagnosis of the satellite power system, which improved the accuracy of fault diagnosis. The study found that when the method of qualitative model is used to diagnose faults of the satellite power system, this method only pays attention to the direct output of all parts and the direct connection of parts, but ignored the connection of related parts researched from the system level. Therefore, some report omission phenomena will occur.

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Fault Diagnosis and Fault-Tolerant Control of Wind Turbines via sliding mode control

Fault Diagnosis and Fault-Tolerant Control of Wind Turbines via sliding mode control

In this subsection, as a first step, we aim to analyze the behavior of the fault currents (Ifd and Ifq), which has different signatures for three different fault positions (phase 'a', 'b', or 'c'). Those analysis results are crucial to be used later for the fault position diagnosis. For that purpose, a short circuit fault is introduced into stator phase 'a', 'b' and 'c', respectively, and for each case the simulations results are presented in Figure 3

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