Gear Vibration Analysis and Gear Fault Diagnosis based on Mathematical Morphology

Gear vibration analysis and gear fault diagnosis are related to the multi-objective decision-making process of machinery equipment production, in which a large amount of data and information should be collected, and the relationship between supply/demand needs and available resources, between production and labor, and between enterprise benefit and social benefit should be balanced generally. Thus, the gear fault diagnosis technologies as well as the professional quality and technical quality are required to be very high. To conform to the forward development of mathematical modeling technology, it is urgent to implement safety product management with computer by using gear vibration analysis and gear fault diagnosis as methods for aiding the research and development of machinery gear fault diagnosis system. 7

Fuzzy Fault Diagnosis of a Diesel Engine Non-start

The diesel locomotive plays an important role in the field of transport, and the engine maintenance work is the prerequisite and gnarantee for the locomotive normal working. In this paper, we first establish the fault tree model of locomotive engine 16V240ZJ on the basis of engine non-start as the top event. Then we combines the fitzzy mathematics the- ory and fault tree analysis method for failure diagnosis of 16V240ZJ engine＇s abnormal start-up. We obtained the fuzzy probability curve and top events probability confidence interval by analyzing the fuzzy fault tree qualitatively and quantitatively. It provides a fuzzy analysis basis for solving the problem of 16V240ZJ engine＇s abnormal start-up.

Establishment and Optimization of State Feature System of Diesel Engine Fault Diagnosis

For too many state features are used in the diesel engine state evaluation and fault diagnosis, it is not easy to obtain the rational eigenvalues. In the paper, the cylinder subassembly of diesel engine is used to search for the method of establishing state feature system and optimal approach. The signal of diesel engine has been collected when the piston ring and airtight ring are working at different states, then with the Bootstrap method and Genetic Algorithm (GA), an optimum parameter combination is received. Example shows this method is simple and efficient for establishing diesel engine state feature system, Thus, this method is valuable for the virtual state evaluation of similar complex system.

Fault Diagnosis for Manifold Absolute Pressure Sensor(MAP) of Diesel Engine Based on Elman Neural Network Observer

Intake system of diesel engine is a strong nonlinear system, and it is difficult to establish accurate model of intake system; and bias fault and precision degradation fault of MAP of diesel engine can＇t be diagnosed easily using model-based methods. Thus, a fault diagnosis method based on Elman neural network observer is proposed. By comparing simulation results of intake pressure based on BP network and Elman neural network, lower sampling error magnitude is gained using Elman neural network, and the error is less volatile. Forecast accuracy is between 0.015？0.017 5 and sample error is controlled within 0？0.07. Considering the output stability and complexity of solving comprehensively, Elman neural network with a single hidden layer and with 44 nodes is presented as intake system observer. By comparing the relations of confidence intervals of the residual value between the measured and predicted values, error variance and failures in various fault types. Then four typical MAP faults of diesel engine can be diagnosed： complete failure fault, bias fault, precision degradation fault and drift fault. The simulation results show： intake pressure is observable and selection of diagnostic strategy parameter reasonably can increase the accuracy of diagnosis;the proposed fault diagnosis method only depends on data and structural parameters of observer, not depends on the nonlinear model of air intake system. A fault diagnosis method is proposed not depending system model to observe intake pressure, and bias fault and precision degradation fault of MAP of diesel engine can be diagnosed based on residuals.

Fault diagnosis of diesel engine valve clearance under variable operating condition based on soft interval SVM

The fault detection and diagnosis of diesel engine valve clearance can effectively improve the availability and safety of diesel engine and have extremely important value and significance.Diesel engines generally operate in various stable operating conditions,which have important influence on the fault diagnosis.However,many fault diagnosis methods have been put forward under specific stable operating condition based on vibration signal.As the result of great impact caused by operating conditions,corresponding diagnosis models cannot deal with the fault diagnosis under different operating conditions with required accuracy.In this paper,a fault diagnosis of diesel engine valve clearance under variable operating condition based on soft interval support vector machine(SVM)is proposed.Firstly,the fault features with weak condition sensitivity have been extracted according to the influence analysis of fault on vibration signal.Moreover,soft interval constraint has been applied to SVM algorithm to reduce the random influence of vibration signal on fault features.In addition,different machine learning algorithms based on different feature sets are adopted to conduct the fault diagnosis under different operating conditions for comparison.Experimental results show that the proposed method is applicable for fault diagnosis under variable operating condition with good accuracy.

SVM Algorithm for Vibration Fault Diagnosis in Centrifugal Pump

Vibration failure in the pumping system is a significant issue for indus-tries that rely on the pump as a critical device which requires regular maintenance.To save energy and money,a new automated system must be developed that can detect anomalies at an early stage.This paper presents a case study of a machine learning(ML)-based computational technique for automatic fault detection in a cascade pumping system based on variable frequency drive(VFD).Since the intensity of the vibrational effect depends on which axis has the most significant effect,a three-axis accelerometer is used to measure it in the pumping system.The emphasis is on determining the vibration effect on different axes.For experiment,various ML algorithms are investigated on collected vibratory data through Matlab software in x,y,z axes and performances of the algorithms are compared based on accuracy rate,prediction speed and training time.Based on the proposed research results,the multiclass support vector machine(MSVM)is found to be the best suitable algorithm compared to other algorithms.It has been demonstrated that ML algorithms can detect faults automatically rather than conventional meth-ods.MSVM is used for the proposed work because it is less complex and pro-duces better results with a limited data set.

Diagnosis of Valve-Slap of Diesel Engine with EEMD-EMD-AGST Approach

A hybrid of ensemble empirical mode decomposition and empirical mode decomposition (EEMD-EMD) is introduced to diagnose the valve-slap vibration signal,which is relative to the dominant combustion knock vibration signal given out by a diesel engine around the top dead center (TDC).The time-frequency representations of intrinsic mode functions (IMFs) decomposed by EEMD-EMD are obtained by adaptive generalized S transform (AGST).A type 493 diesel engine was used for the experiment,and the result indicates that the valve-slap of the diesel engine is serious,and the vibration frequencies are higher than the combustion knock.With EEMD-EMD-AGST approach,the valve-slap can be identified by the vibration analysis of the diesel engine.

Fault diagnosis of a marine power-generation diesel engine based on the Gramian angular field and a convolutional neural network

Marine power-generation diesel engines operate in harsh environments.Their vibration signals are highly complex and the feature information exhibits a non-linear distribution.It is difficult to extract effective feature information from the network model,resulting in low fault-diagnosis accuracy.To address this problem,we propose a fault-diagnosis method that combines the Gramian angular field(GAF)with a convolutional neural network(CNN).Firstly,the vibration signals are transformed into 2D images by taking advantage of the GAF,which preserves the temporal correlation.The raw signals can be mapped to 2D image features such as texture and color.To integrate the feature information,the images of the Gramian angular summation field(GASF)and Gramian angular difference field(GADF)are fused by the weighted average fusion method.Secondly,the channel attention mechanism and temporal attention mechanism are introduced in the CNN model to optimize the CNN learning mechanism.Introducing the concept of residuals in the attention mechanism improves the feasibility of optimization.Finally,the weighted average fused images are fed into the CNN for feature extraction and fault diagnosis.The validity of the proposed method is verified by experiments with abnormal valve clearance.The average diagnostic accuracy is 98.40%.When−20 dB≤signal-to-noise ratio(SNR)≤20 dB,the diagnostic accuracy of the proposed method is higher than 94.00%.The proposed method has superior diagnostic performance.Moreover,it has a certain anti-noise capability and variable-load adaptive capability.

Detection of cylinder pressure in diesel engines using cylinder head vibration and time series methods

This paper investigates the vibration characteristics of diesel engine cylinder heads by means of the time series method. With the concept of "Assumed System",the vibration transfer function of real cylinder head structures is established using the autoregressive-moving average models(ARMA models) of cylinder head surface vibration signals. Then this transfer function is successfully used to reconstruct the gas pressure trace inside the cylinder from measured cylinder head vibration signals. This offers an effective means for diesel engine cylinder pressure detection and condition monitoring.

Complete Modeling for Systems of a Marine Diesel Engine

This paper presents a simulator model of a marine diesel engine based on physical, semi-physical, mathematical and thermodynamic equations, which allows fast predictive simulations The whole engine system is divided into several functional blocks： cooling, lubrication, air, injection, combustion and emissions. The sub-models and dynamic characteristics of individual blocks are established according to engine working principles equations and experimental data collected from a marine diesel engine test bench for SIMB Company under the reference 6M26SRP1. The overall engine system dynamics is expressed as a set of simultaneous algebraic and differential equations using sub-blocks and S-Functions of Matlab/Simulink. The simulation of this model, implemented on Matlab/Simulink has been validated and can be used to obtain engine performance, pressure, temperature, efficiency, heat release, crank angle, fuel rate, emissions at different sub-blocks. The simulator will be used, in future work, to study the engine performance in faulty conditions, and can be used to assist marine engineers in fault diagnosis and estimation （FDI） as well as designers to predict the behavior of the cooling system, lubrication system, injection system, combustion, emissions, in order to optimize the dimensions of different components. This program is a platform for fault simulator, to investigate the impact on sub-blocks engine＇s output of changing values for faults parameters such as： faulty fuel injector, leaky cylinder, worn fuel pump, broken piston rings, a dirty turbocharger, dirty air filter, dirty air cooler, air leakage, water leakage, oil leakage and contamination, fouling of heat exchanger, pumps wear, failure of injectors （and many others）.

Data fusion for fault diagnosis using multi-class Support Vector Machines

Multi-source multi-class classification methods based on multi-class Support Vector Machines and data fusion strategies are proposed in this paper. The centralized and distributed fusion schemes are applied to combine information from several data sources. In the centralized scheme, all information from several data sources is centralized to construct an input space. Then a multi-class Support Vector Machine classifier is trained. In the distributed schemes, the individual data sources are proc-essed separately and modelled by using the multi-class Support Vector Machine. Then new data fusion strategies are proposed to combine the information from the individual multi-class Support Vector Machine models. Our proposed fusion strategies take into account that an Support Vector Machine (SVM) classifier achieves classification by finding the optimal classification hyperplane with maximal margin. The proposed methods are applied for fault diagnosis of a diesel engine. The experimental results showed that almost all the proposed approaches can largely improve the diagnostic accuracy. The robustness of diagnosis is also improved because of the implementation of data fusion strategies. The proposed methods can also be applied in other fields.

Brake Fault Diagnosis Through Machine Learning Approaches–A Review

Diagnosis is the recognition of the nature and cause of a certain phenomenon.It is generally used to determine cause and effect of a problem. Machine fault diagnosis isa field of finding faults arising in machines. To identify the most probable faults leadingto failure, many methods are used for data collection, including vibration monitoring,thermal imaging, oil particle analysis, etc. Then these data are processed using methodslike spectral analysis, wavelet analysis, wavelet transform, short-term Fourier transform,high-resolution spectral analysis, waveform analysis, etc. The results of this analysis areused in a root cause failure analysis in order to determine the original cause of the fault.This paper presents a brief review about one such application known as machine learningfor the brake fault diagnosis problems.

A Comprehensive 3-Steps Methodology for Vibration-Based Fault Detection,Diagnosis and Localization in Rotating Machines

In any industry,it is the requirement to know whether the machine is healthy or not to operate machine further.If the machine is not healthy then what is the fault in the machine and then finally its location.The paper is proposing a 3-Steps methodology for the machine fault diagnosis to meet the industrial requirements to aid the maintenance activity.The Step-1 identifies whether machine is healthy or faulty,then Step-2 detect the type of defect and finally its location in Step-3.This method is extended further from the earlier study on the 2-Steps method for the rotor defects only to the 3-Steps methodology to both rotor and bearing defects.The method uses the optimised vibration parameters and a simple Artificial Neural Network(ANN)-based Machine Learning(ML)model from the earlier studies.The model is initially developed,tested and validated on an experimental rotating rig operating at a speed above 1st critical speed.The proposed method and model are then further validated at 2 different operating speeds,one below 1st critical speed and other above 2nd critical speed.The machine dynamics are expected to be significantly different at these speeds.This highlights the robustness of the proposed 3-Steps method.

Research on the applications of infrared technique in the diagnosis and prediction of diesel engine exhaust fault

This paper mainly introduces the basic principles,the methods and the applications of infrared technique in the diagnosis and prediction of diesel engine exhaust faults. The test-bed for monitoring diesel engine exhaust faults by thermal infrared imager has been designed. In different running conditions, the exterior surface radiation temperatures of the exhaust pipe of the 6135G-1 diesel engine have been measured by infrared imaging system. According to the principle of infrared temperature measurement, the real temperatures of the exterior surface of the exhaust pipe have been calculated. Based on the principle of heat transfer, the method of calculating the exhaust temperatures according to the exterior surface radiation temperatures of exhaust pipe measured by thermal infrared imager is built. The relationship between diesel engine exhaust temperatures and faults has been analyzed. It is shown that the application of infrared inspection and diagnosis to the identifying of diesel engine exhaust faults is feasible and effective.

Fault Diagnosis for a Diesel Valve Train Based on Time-Freq uency Analysis and Probabilistic Neural Networks

The cone-shaped kernel distributions of vibration acceleration signals, whichwere acquired from the cylinder head in eight different states of a valve train, were calculatedand displayed in grey images. Probabilistic Neural Networks ( PAW) was used to classify the imagesdirectly after the images were normalized. By this way, the problem of fault diagnosis for a valvetrain was transferred to the classification of time-frequency images. As there is no need to extractfeatures from time-frequency images before classification, the fault diagnosis process is highlysimplified. The experimental results show that the vibration signals can be classified accurately bythe proposed methods.

A Diagnosis Method of Vibration Fault of a Steam Turbine Based on Information Entropy and Grey Correlation Analysis

The vibration fault, one of the common faults in the steam turbine generator unit, brings great damage to the production and the running process. It is well known that the information entropy is to describe the degree of indeterminacy of the system, so the information entropy can be used to measure Despite its efficiency, one kind of information entropy is just enabled to identify make up for this limitation, based on nalysis was studied for vibration fault the vibration condition of the unit. certain part of the faults. In order to the faulty signals collected from the rotor test platform, the grey correlation adiagnosis of steam turbine shafting in this paper. The reference faulty matrix and the calculation model of grey correlation degree was established based on three kinds of information entropy. The analysis shows that grey correlation analysis is a useful method for fault diagnosis of shafting and can be used as a quantitative index for fault diagnosis.

Fault diagnosis of tractor auxiliary gearbox using vibration analysis and random forest classifier

Accurate detection of mechanical components faults is an essential step for reduction of repair cost,human injury probability and loss of production.Using intelligent fault diagno-sis systems in tractor could prevent secondary damage,thereby avoiding heavy conse-quences.In this study,fault diagnosis of tractor auxiliary gearbox is presented.Vibration signals of healthy and faulty pinions gear under three different operational conditions(Rotational speeds of 600 RPM,1350 RPM and 2000 RPM)were collected,and discrete wave-let transform(DWT)was used as signal processing.Useful statistical features were calcu-lated from collected signals.Correlation-based feature selection(CFS)method was used to find the best features.Random forest(RF)and multilayer perceptron(MLP)neural net-works were employed to classify the data.The overall accuracy of RF classifier without using feature selection were 86.25%,at 600 RPM.The corresponding values of RF trained with the optimal 6 features by using CFS was 92.5%.The best results obtained at 1350 RPM,since the detection accuracy was 95%.The results of this study demonstrated the effectiveness and feasibility of the proposed method for fault diagnosis of tractor auxiliary gearbox.

Fault Detection and Diagnosis of a Gearbox in Marine Propulsion Systems Using Bispectrum Analysis and Artificial Neural Networks

A marine propulsion system is a very complicated system composed of many mechanical components.As a result,the vibration signal of a gearbox in the system is strongly coupled with the vibration signatures of other components including a diesel engine and main shaft.It is therefore imperative to assess the coupling effect on diagnostic reliability in the process of gear fault diagnosis.For this reason,a fault detection and diagnosis method based on bispectrum analysis and artificial neural networks (ANNs) was proposed for the gearbox with consideration given to the impact of the other components in marine propulsion systems.To monitor the gear conditions,the bispectrum analysis was first employed to detect gear faults.The amplitude-frequency plots containing gear characteristic signals were then attained based on the bispectrum technique,which could be regarded as an index actualizing forepart gear faults diagnosis.Both the back propagation neural network (BPNN) and the radial-basis function neural network (RBFNN) were applied to identify the states of the gearbox.The numeric and experimental test results show the bispectral patterns of varying gear fault severities are different so that distinct fault features of the vibrant signal of a marine gearbox can be extracted effectively using the bispectrum,and the ANN classification method has achieved high detection accuracy.Hence,the proposed diagnostic techniques have the capability of diagnosing marine gear faults in the earlier phases,and thus have application importance.

Improved deep residual shrinkage network for a multi-cylinder heavy-duty engine fault detection with single channel surface vibration

The health monitoring and fault diagnosis of heavy-duty engines are increasingly important for energy storage ecosystem. During operation, vibration characters corresponding to the specific fault need to be extracted from the overall system vibration. Faulty characteristics emanating from one single cylinder are also mixed with those from other cylinders. Besides, the change of working condition brings strong nonlinearities in surface vibration. To solve these problems, an improved deep residual shrinkage network (IDRSN) is developed for detecting diverse engine faults at various degrees using single channel surface vibration signal. Within IDRSN, a wide convolution kernel is utilized in first convolution layer to capture the long-term fault-related impacts and eliminate the short-time random impact. The residual network module is adopted to enhance the focus the relevant components of vibration signals. Mini-batch training strategy is used to improve the model stability. Meanwhile, Gradient-weighted class activation map is adopted to assess the consistency between the learned knowledge and the fault-related information. The IDRSN is implemented to diagnosing a diesel engine under various faults, faulty degrees and operating speeds. Comparisons with existing models are analyzed in terms of hyper-parameters, training samples, noise resistance, and visualization. Results demonstrate the proposed IDRSN's superior performance on fault diagnosis accuracy, stability, anti-noise performance, and anti-interference performance. An average accuracy rate of 98.38 % was achieved by the proposed IDRSN, in comparison to 96.64 % and 93.56 % achieved by the DRSN and the wide-kernel deep convolutional neural network respectively. These results highlight the proposed IDRSN's superiority in diagnosing multiple faults under various working conditions, offering a low-cost, highly effective, and applicable approach for complex fault diagnosis tasks.

Fault Diagnosis of Cylindrical Grinding Machine

Based on experiment modal analysis(EMA) and operation modal analysis(OMA), the dynamic characteristics of cylindrical grinding machine were measured and provided a basis for further failure analysis.The influences of grinding parameters on dynamic characteristics were studied by analyzing the diagnostic signals extracted from racing and grinding experiments.The significant frequency of 38 Hz related to grinding wheel spindle speed of 2 307 r/min showed that the wheel spindle system was in a state of imbalan...