New Method For Fault Diagnosis Research Articles

A new method of fault diagnosis for aeroengines with dispersedly clumped gas path parameters

In an aircraft fleet with multiple aeroengines in service, the gas path parameter measurement values from engines of the same type are dispersed within a certain range due to manufacturing technology, assembly tolerances, operation conditions, and maintenance. This dispersion cannot be ignored and makes it impossible to apply unified health diagnosis criteria to all engines. To address this issue, a fault diagnosis solution for aeroengines with dispersedly clumped gas path parameters based on meta aero-engine is proposed. The concept of meta aero-engine is proposed to derive an engine group with dispersedly clumped parameters. To diagnose engines, a sphenophyllum classifier is proposed, which has a unified judgement criterion. A new weak classifier ensemble method based on divide-and-conquer is proposed to enhance the classification ability of the sphenophyllum classifier. The results of the basic classifier are partitioned and then analyzed in combination with the results of other classifiers in the classifier ensemble method. The effectiveness of the proposed method is verified through turbofan engine simulation dataset. The proposed method has strong interpretability for engine fleet with dispersedly clumped gas path parameters.

A Fault Diagnosis Tool for Electrohydraulic Actuators

Abstract This letter introduces a new method for fault diagnosis of electrohydraulic actuators (EHA). Common faults include abnormal supply pressure, viscous friction, bulk modulus, and oil leakage. First, a validated model of an EHA is used to simulate faults at varying percent perturbations to generate data. The harmonics of the corresponding servo-valve control signal are calculated using sliding discrete Fourier transform (DFT) and stored. A fully connected neural network is then trained to predict fault cases using harmonic information. Simulation results show high accuracy for diagnosis of individual cases and ability to diagnose mixed cases. The actuator motion reference signal is also shown to impact overall diagnosability. Finally, the method was tested on experimental data consisting of nominal and low-pressure cases. Random disturbances were added to simulation training data to allow more robustness and resulted in 80% accuracy on the 10 experimental datapoints. It is demonstrated that a validated model can be used to diagnose faults in actual experimental setup. Future work will be done to diagnose non-pressure experimental cases.

An Intelligent Diagnostic Method for Multisource Coupling Faults of Complex Mechanical Systems

In the actual environment, there are difficult points such as complicated mechanical system fault types, random fault locations, and inconspicuous minor fault signals, which make it difficult to accurately diagnose faults. This paper proposes a new method for fault diagnosis of an adaptive multisensor bearing-gear system based on GAF/MTF (Gramian angular fields and Markov transition fields) and ResNet (deep residual network). First, we establish a multisensor signal acquisition system to monitor the running signals of the bearing-gearbox composite test bench in real time. Faulty parts include multiple types of composite faults of different sizes, different fault types, and different transmission stages. Second, based on GAF/MTFs, the multichannel timing signal collected by using the acquisition system is converted into multichannel pictures, and pictures are fused and compressed into three-channel pictures. Finally, we input these pictures into ResNet for fault diagnosis. The experimental results show that the GAF/MTF-ResNet model has a recognition accuracy of 72.14% for a total of 520 classification label test sets under different motor speeds, different sampling times, and different types of faults. Among them, the accuracy of the motor speed and sampling time is close to 100%, and the accuracy of gearbox failure and bearing failure is 75.25% and 88.97%, respectively. This shows that the method provides new ideas for the composite fault diagnosis of mechanical systems under different working conditions and different types of faults and has theoretical guiding significance.

A New Method for Fault Diagnosis of Hydraulic System Based on Improved Empirical Wavelet Transform and Kernel Extreme Learning Machine

A New Method for Fault Diagnosis of Hydraulic System Based on Improved Empirical Wavelet Transform and Kernel Extreme Learning Machine

A novel method for diagnosing rolling bearing faults based on the frequency spectrum distribution of the modulation signal

Bearing fault diagnosis is required to monitor the running status of rolling bearings, and can greatly reduce the loss caused by rolling bearing faults. It is a very important aspect of prognostic and health management. In this paper, a new method for fault diagnosis, based on an improved fast kurtogram and novel envelope spectrum analysis, is proposed to diagnose rolling bearing faults. In the proposed method, the improved fast kurtogram method is used to select the center frequency and bandwidth of the optimal signal filter which is used to filter the raw bearing vibration signals. Then, the filtered signal is transformed to the frequency domain. Novel envelope spectrum analysis is used to analyze the amplitude distribution of the envelope spectrum waveforms in order to extract more useful features from different zones rather than the whole frequency domain. The extracted features are used to calculate the fitting ratio for diagnosing bearing faults. The proposed method is validated on the fault data of rolling bearings provided by CWRU and QPZZ-II platforms. The experimental results show that the proposed method can efficiently extract features and diagnose rolling bearing faults.

A Fault Diagnosis Method of Rolling Bearing Integrated with Cooperative Energy Feature Extraction and Improved Least-Squares Support Vector Machine

To solve the problem that the bearing fault of variable working conditions is challenging to identify and classify in the industrial field, this paper proposes a new method based on optimization of multidimension fault energy characteristics and integrates with an improved least-squares support vector machine (LSSVM). First, because the traditional wavelet energy feature is difficult to effectively reflect the characteristics of rolling bearing under different working conditions, based on analyzing the wavelet energy feature extraction in detail, a collaborative method of multidimension fault energy feature extraction combined with the method of Transfer Component Analysis (TCA) is constructed, which improves the discrimination between the different features and the compactness between the same features of rolling bearing faults. Then, for solving the problem of the local optimal of particle swarm optimization (PSO) in fault diagnosis and recognition of rolling bearing, an improved LSSVM based on particle swarm optimization and wavelet mutation optimization is established to realize the collaborative optimization and adjustment of LSSVM dynamic parameters. Based on the improved LSSVM and optimization of multidimensional energy characteristics, a new method for fault diagnosis of rolling bearing is designed. Finally, the simulation and analysis of the proposed algorithm are verified by the experimental data of different working conditions. The experimental results show that this method can effectively extract the multidimensional fault characteristics under variable working conditions and has a high fault recognition rate.

Open-circuit fault diagnosis of traction inverter based on compressed sensing theory

This study proposes a new method of fault diagnosis based on the least squares support vector machine with gradient information (G-LS-SVM) to solve the insulated-gate bipolar transistor(IGBT) open-circuit failure problem of the traction inverter in a catenary power supply system. First, a simulation model based on traction inverter topology is built, and various voltage fault signal waveforms are simulated based on the IGBT inverter open-circuit fault classification. Second, compressive sensing theory is used to sparsely represent the voltage fault signal and make it a fault signal. The new method has a high degree of sparseness and builds an overcomplete dictionary model containing the feature vectors of voltage fault signals based on a double sparse dictionary model to match the sparse signal characteristics. Finally, the space vector transform is used to represent the three-phase voltage scalar in the traction inverter as a composite quantity to reduce the redundancy of the fault signals and data-processing capabilities. A G-LS-SVM fault diagnosis model is then built to diagnose and identify the voltage fault signal feature vector in an overcomplete dictionary. The simulation results show that the accuracy of this method for various types of IGBT tube fault diagnosis is over 98.92%. Moreover, the G-LS-SVM model is robust and not affected by Gaussian white noise.

Fault Management Based on Systems Description as Directed Graph With Absolute Dependence Relations

A new systems description method that utilizes graph theory for systems modeling is presented. The new method represents the system in terms of a directed graph, where each system component is represented by a vertex and dependencies between system components are represented by directed edges, also called arcs. The absolute dependence rule is introduced as the criterion for the analysis of a system into the graph representation. Together with the concept of an elementary component of a system, the proposed systems description method is applicable to a wide variety of systems for fault diagnosis, reliability engineering, and other system modeling and system architecture design purposes. The application of the new method for fault diagnosis is demonstrated in this paper by developing a novel fault management algorithm, and connections with several other systems description methods are presented.

Research on Fault Extraction Method of Variational Mode Decomposition Based on Immunized Fruit Fly Optimization Algorithm.

In recent years, a new method of fault diagnosis, named variational mode decomposition (VMD), has been widely used in industrial production, but the decomposition accuracy of VMD is determined by two parameters, which are respectively the decomposition layer number k and the penalty factor α, if the parameters are not properly selected, there will be over-decomposition or under-decomposition. In order to find an approach to determine the parameters adaptively, a method to optimize VMD by using the immune fruit fly optimization algorithm (IFOA) is proposed in this paper. In this method, permutation entropy is used as the fitness function, firstly, the immune fruit fly optimization algorithm is used to search the combined parameters of k and α in VMD, searching for the best combination parameters of k and α by iteration, and then uses the combined parameters to perform VMD, finally, the center frequency is determined through frequency spectrum analysis. The method mentioned is applied to the fault extraction of a simulated signal and a measured signal of a wind turbine gearbox, and the fault frequency is successfully extracted. Using ensemble empirical mode decomposition (EEMD) and singular spectrum decomposition (SSD) to compare with the proposed method, which validated feasibility of the proposed method.

Investigation of a multi-sensor data fusion technique for the fault diagnosis of gearboxes

Gearbox is the key functional unit in a mechanical transmission system. As its operating condition being complex and the interference transmitting from diverse paths, the vibration signals collected from an individual sensor may not provide a fully accurate description on the health condition of a gearbox. For this reason, a new method for fault diagnosis of gearboxes based on multi-sensor data fusion is presented in this paper. There are three main steps in this method. First, prior to feature extraction, two signal processing methods, i.e. the energy operator and time synchronous averaging, are applied to multi-sensor vibration signals to remove interference and highlight fault characteristic information, then the statistical features are extracted from both the raw and preprocessed signals to form an original feature set. Second, a coupled feature selection scheme combining the distance evaluation technique and max-relevance and min-redundancy is carried out to obtain an optimal feature set. Finally, the deep belief network, a novel intelligent diagnosis method with a deep architecture, is applied to identify different gearbox health conditions. As the multi-sensor data fusion technique is utilized to provide sufficient and complementary information for fault diagnosis, this method holds the potential to overcome the shortcomings from an individual sensor that may not accurately describe the health conditions of gearboxes. Ten different gearbox health conditions are simulated to validate the performance of the proposed method. The results confirm the superiority of the proposed method in gearbox fault diagnosis.

Fault Diagnosis of Power Transformers With Membership Degree

Power transformers are important equipment for power systems, and a dissolved gas analysis (DGA) is widely used to detect incipient faults in oil-pregnant transformers. The conventional methods are prone to misinterpreting the gas data near the boundaries and the correct rate is low. Though a high correct rate is reported with intelligent methods as artificial neural network, support vector machine, and so on, these methods are usually too complicated to be implemented practically on a wide range. Based on clustering techniques, this paper proposes a new method for fault diagnosis of transformers with the DGA. A reference fault set is provided, and the fault diagnosis is implemented by calculating the membership of the DGA data to the reference fault set. Test with credible DGA dataset (201 field cases) shows that the correct rate of the new method is 89%, while the David triangle method is 79% and the IEC ratio method is 59%, which demonstrate the superiority of the proposed method to the conventional ones. The new method is simple and highly accurate, indicating a good application prospect in engineering practice.

Fault diagnosis approach of rolling bearing based on NA‐MEMD and FRCMAC

This paper proposed a new method of fault diagnosis based on Noise Assisted Multivariate Empirical Mode Decomposition (NA-MEMD) and Fuzzy Recurrent Cerebellar Model Articulation Controller (FRCMAC) Neural Networks. Aiming at the problem that during the use of the NA-MEMD method, the white noise amplitude parameter needs to be selected by artificial experience, a method of using Genetic Algorithm (GA) to optimize its auxiliary white noise parameters is proposed, which facilitates the use of NA-MEMD. We proposed a novel FRCMAC structure which improved Learning efficiency and dynamic response speed than traditional CMAC structure. First, the GA-NA-MEMD method is applied to process the vibration signals of rolling bearings, and the signals are decomposed into a group of Intrinsic Mode Functions (IMFs). Then use energy moments of IMFs as fault feature vectors to train FRCMAC neural network, a neural network structure suitable for rolling bearing fault diagnosis is obtained. Finally, the data from bearing data center of Case Western Reserve University is used to prove that the fault diagnosis method proposed in this paper is superior to other methods in diagnosis time and precision, which can meet the training requirements more quickly with limited training samples and fault diagnosis results more accurate.

A Novel Adaptive Signal Processing Method Based on Enhanced Empirical Wavelet Transform Technology.

Empirical wavelet transform (EWT) is a novel adaptive signal decomposition method, whose main shortcoming is the fact that Fourier segmentation is strongly dependent on the local maxima of the amplitudes of the Fourier spectrum. An enhanced empirical wavelet transform (MSCEWT) based on maximum-minimum length curve method is proposed to realize fault diagnosis of motor bearings. The maximum-minimum length curve method transforms the original vibration signal spectrum to scale space in order to obtain a set of minimum length curves, and find the maximum length curve value in the set of the minimum length curve values for obtaining the number of the spectrum decomposition intervals. The MSCEWT method is used to decompose the vibration signal into a series of intrinsic mode functions (IMFs), which are processed by Hilbert transform. Then the frequency of each component is extracted by power spectrum and compared with the theoretical value of motor bearing fault feature frequency in order to determine and obtain fault diagnosis result. In order to verify the effectiveness of the MSCEWT method for fault diagnosis, the actual motor bearing vibration signals are selected and the empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) methods are selected for comparative analysis in here. The results show that the maximum-minimum length curve method can enhance EWT method and the MSCEWT method can solve the shortcomings of the Fourier spectrum segmentation and can effectively decompose the bearing vibration signal for obtaining less number of intrinsic mode function (IMF) components than the EMD and EEMD methods. It can effectively extract the fault feature frequency of the motor bearing and realize fault diagnosis. Therefore, the study provides a new method for fault diagnosis of rotating machinery.

Research on SDG Fault Diagnosis of Ocean Shipping Boiler System Based on Fuzzy Granular Computing Under Data Fusion

Abstract The research work in this paper belongs to the application of granular computing, graph theory and its application in fault detection and diagnosis. It is a cross cutting and frontier research field in computer science, information science and graph theory. The results of this paper are of great significance to the application of the fault detection and diagnosis of the ocean boilers system. This research combines granular computing theory and signed directed graph, and proposes a new method of fault diagnosis, and applies it to the fault diagnosis of ocean ship boiler system.

A novel intelligent diagnosis method using optimal LS-SVM with improved PSO algorithm

Aiming at the problem that the most existing fault diagnosis methods could not effectively recognize the early faults in the rotating machinery, the empirical mode decomposition, fuzzy information entropy, improved particle swarm optimization algorithm and least squares support vector machines are introduced into the fault diagnosis to propose a novel intelligent diagnosis method, which is applied to diagnose the faults of the motor bearing in this paper. In the proposed method, the vibration signal is decomposed into a set of intrinsic mode functions (IMFs) by using empirical mode decomposition method. The fuzzy information entropy values of IMFs are calculated to reveal the intrinsic characteristics of the vibration signal and considered as feature vectors. Then the diversity mutation strategy, neighborhood mutation strategy, learning factor strategy and inertia weight strategy for basic particle swarm optimization (PSO) algorithm are used to propose an improved PSO algorithm. The improved PSO algorithm is used to optimize the parameters of least squares support vector machines (LS-SVM) in order to construct an optimal LS-SVM classifier, which is used to classify the fault. Finally, the proposed fault diagnosis method is fully evaluated by experiments and comparative studies for motor bearing. The experiment results indicate that the fuzzy information entropy can accurately and more completely extract the characteristics of the vibration signal. The improved PSO algorithm can effectively improve the classification accuracy of LS-SVM, and the proposed fault diagnosis method outperforms the other mentioned methods in this paper and published in the literature. It provides a new method for fault diagnosis of rotating machinery.

Application of a modeling approach on a cyber-physical system "RobAIR"

This paper proposes a new method of fault diagnosis of cyber-physical systems (CPS). The complexity of these systems in heterogeneous technologies require elaborate diagnostic techniques and maintenance. We illustrate our methodology on a telepresence robot "RobAIR" as an example of CPS.First, we applied a knowledge modeling approach on this case study through the representation of four types of knowledge: functional, structural, topological and behavioral. These representations are achieved using a modeling tool "Papyrus". Secondly, from knowledge representation, three algorithms for detection and fault identification are proposed to remove the ambiguity in functions and components. These algorithms will be explained through the RobAIR example.

Fault Diagnosis Technology Research on Autopilot

Aiming at the issues of the autopilot that has many fault types, less data sample and less test interface, this paper analyzes the fault isolation ability of automatic test system in detail. Moreover, the knowledge acquisition method based on simulation is studied so that the existing drive instrument test system is improved. In addition, an improved support vector machine inference algorithm is proposed. Based on this, a new method of fault diagnosis of a testing system is presented. Finally, some examples are given to verify the correctness and validity of the method. The successful application in a certain missile drive device shows that the method can achieve good diagnosis results.

Fault Diagnosis of Electric Transmission Lines using Modular Neural Networks

This paper proposes a new method for fault diagnosis in electric power systems based on neural networks. With this method the diagnosis is performed by assigning a neural module for each type of component of the electric power system, whether it is a transmission line, bus or transformer. The neural modules for buses and transformers comprise two diagnostic levels which take into consideration the logic states of switches and relays, both internal and back-up. The neural module for transmission lines also has a third diagnostic level which takes into account the oscillograms of fault voltages and currents, as well as the frequency spectrums of these oscillograms, in order to verify if the transmission line had in fact been subjected to a fault. One important advantage of the diagnostic system proposed is that its implementation does not require the use of a network configurator for the system; it does not depend on the size of the power network, nor does it require retraining of the neural modules if the power network increases in size, making its application possible to only one component, a specific area, or the whole context of the power system.

Study on multi-fractal fault diagnosis based on EMD fusion in hydraulic engineering

The vibration signal analysis of the hydraulic turbine unit aims at extracting the characteristic information of the unit vibration. The effective signal processing and information extraction are the key to state monitoring and fault diagnosis of the hydraulic turbine unit. In this paper, the vibration fault diagnosis model is established, which combines EMD, multi-fractal spectrum and modified BP neural network; the vibration signal waveform is identified and purified with EMD to obtain approximation coefficient of various fault signals; the characteristic vector of the vibration fault is acquired with the multi-fractal spectrum algorithm, which is classified and identified as input vector of BP neural network. The signal characteristics are extracted through the waveform, the diagnosis and identification are carried out in combination of the multi-fractal spectrum to provide a new method for fault diagnosis of the hydraulic turbine unit. After the application test, the results show that the method can improve the intelligence and humanization of diagnosis, enhance the man–machine interaction, and produce satisfactory identification result.

Research on Application of Blind Source Separation in Rolling Bearing Fault Diagnosis Based on Particle Swarm Optimization

This paper presents blind source separation of rolling bearing based on particle swarm optimization. The algorithm combines the advantages of both blind source separation and particle swarm optimization. Through the experiment it is shown that the algorithm can separate the signals collected from rolling bearing and gearbox effectively, which can provide a new method for fault diagnosis and signal processing of machinery equipment.