Comprehensive Fault Diagnosis Research Articles

A multi-stage hybrid fault diagnosis approach for operating conditions of nuclear power plant

The security and stability of systems and components in operating nuclear power plants (NPPs) are extremely important factors that determine the plants reliability and service life. An essential system that help reduce the maintenance costs and improve the reliability of NPPs is an efficient fault diagnosis system. In this paper, an optimized fault diagnosis framework is proposed to efficiently identify system-level failures and their severities to guarantee the sustainability of NPPs. To facilitate the recognition of the real-time failure types and to extract both the constraint relationships and fault regularities of system-level parameters, the least squares support vector machine (LS-SVM) method was introduced at the fault diagnosis step. The severity assessment to estimate the degree of failures was subsequently performed using a method derived from gaussian process regression (GPR). To overcome the challenge of selecting hyperparameters of GPR, the particle swarm optimization (PSO) was applied to search for the optimal hyperparameters of GPR. The PSO intelligent search strategy implemented to obtain a fault severity assessment model ultimately aided operators to make an informed decision on the operating conditions of the plant. Simulations carried out based on the proposed fault diagnosis framework demonstrate the accuracy and reliability of the methodology, as well as the availability of support for the stable operation of NPPs. This proposed comprehensive fault diagnosis framework is suitable for multi-dimensional monitoring of NPPs.

Application of Particle Swarm Optimization Algorithm in Power Transformer Fault Diagnosis

Fault diagnosis of power transformer is indispensable for power system reliability. To improve the function of fault diagnosis and overcome the “code absence” problem of the traditional ratio method, this paper presents a novel approach for oil chromatographic fault diagnosis based on particle swarm optimization algorithm. The PSO algorithm is used to obtain the optimal three-ratio value that can best represent various fault types of power transformers, and then the change trend of the characteristic gas of the power transformer is analyzed to predict the possible faults. Combining the stereogram method and the optimal three-ratio method, A comprehensive fault diagnosis method for based on oil chromatographic distraction is obtained. In the end, simulation of the actual oil chromatographic data of the transformer verifies the accuracy and effectiveness of the proposed method.

Research on Testability Design and Evaluation Method for Ship Electromechanical Equipment

As a relatively large amount of electromechanical equipment was installed in the ship systems, it was difficult to position and eliminate the faults of the M&E equipment if any. Hence, there was a need to improve the fault detection and isolation capabilities of the equipment depending on testability design and to develop the corresponding fault diagnosis system when necessary, thus improving the overall availability of the equipment and ensuring that the ship could successfully complete the missions. In view of the above requirements, this paper, taking the most representative M&E equipment - pump set on the ship as a research object, gave the testability design process of the M&E equipment - pump set for product development, illustrated the equipment testability design methods from 4 aspects (namely determination of equipment testability and fault diagnosis system requirements, testability analysis & design, comprehensive fault diagnosis system design and testability verification process design), specified the testability design & monitoring object selection principles and fault characteristic determination methods, and finally provided the testability design evaluation methods for the M&E equipment for verifying whether the testability requirements were met.

A Novel Stacked Auto Encoders Sparse Filter Rotating Component Comprehensive Diagnosis Network for Extracting Domain Invariant Features

In recent years, the method of deep learning has been widely used in the field of fault diagnosis of mechanical equipment due to its strong feature extraction and other advantages such as high efficiency, portability, and so on. However, at present, most kinds of intelligent fault diagnosis algorithms mainly focus on the diagnosis of a single fault component, and few intelligent diagnosis models can simultaneously carry out comprehensive fault diagnosis for a rotating system composed of a shaft, bearing, gear, and so on. In order to solve this problem, a novel stacked auto encoders sparse filter rotating component comprehensive diagnosis network (SAFC) was proposed to extract domain invariant features of various health conditions at different speeds. The model clusters domain invariant features at different speeds through the self-coding network, and then classifies fault types of various parts through sparse filtering. The SAFC model was validated by the vibration data collected, and the results show that this model has higher diagnostic performance than other models.

Memory Physical Aware Multi-Level Fault Diagnosis Flow

Advanced methods of fault detection and diagnosis become increasingly important for the improvement of reliability, safety and efficiency in nanoscale designs. Because the existing approaches do not give a deeper insight and usually do not allow a comprehensive fault diagnosis, multi-level model based methods of fault detection were developed by using hierarchy of detection and diagnosis methods. This contribution proposes a memory physical (scrambling) aware multi-level fault diagnosis flow which is generic and applicable both for planar- and FinFET-based memories. In addition, special test algorithms for classification of static and dynamic faults are discussed while for classification of FinFET-specific faults a new test algorithm March FFDD is proposed. The flow is validated on 16nm FPGA board as well as it has been applied to numerous chips enabling successful physical failure analysis (PFA). At the end of the paper some real-life case scenarios of the flow application are presented.

Input Feature Mappings-Based Deep Residual Networks for Fault Diagnosis of Rolling Element Bearing With Complicated Dataset

Most rolling element bearing (REB) fault diagnosis algorithms are evaluated on the Case Western Reserve University (CWRU) bearing dataset for its popularity and simplicity. However, the diagnosis accuracy on CWRU bearing dataset is overly saturated; it is nearly up to 100%. In this study, an input feature mappings (IFMs)-based deep residual network (ResNet) is proposed to conduct detailed and comprehensive fault diagnosis on REB with complicated bearing dataset. Firstly, a new data preprocessing method named as a signal-to-IFMs method is proposed to automatically extract features from raw signals without predefined parameters. Then, a deep ResNet is used as the fault classifier to learn the discriminative features from IFMs and identify the faults of REB. Finally, the proposed model is evaluated on the artificial, real, and mixed damages of the Paderborn university bearing dataset. The proposed method yields the average testing accuracies of 99.7%, 99.7%, and 99.81% in artificial, real, and mixed bearing damages, which outperforms other methods.

Design and Analysis for Early Warning of Rotor UAV Based on Data-Driven DBN

The unmanned aerial vehicle (UAV), which is a typical multi-sensor closed-loop flight control system, has the properties of multivariable, time-varying, strong coupling, and nonlinearity. Therefore, it is very difficult to obtain an accurate mathematical diagnostic model based on the traditional model-based method; this paper proposes a UAV sensor diagnostic method based on data-driven methods, which greatly improves the reliability of the rotor UAV nonlinear flight control system and achieves early warning. In order to realize the rapid on-line fault detection of the rotor UAV flight system and solve the problems of over-fitting, limited generalization, and long training time in the traditional shallow neural network for sensor fault diagnosis, a comprehensive fault diagnosis method based on deep belief network (DBN) is proposed. Using the DBN to replace the shallow neural network, a large amount of off-line historical sample data obtained from the rotor UAV are trained to obtain the optimal DBN network parameters and complete the on-line intelligent diagnosis to achieve the goal of early warning as possible as quickly. In the end, the two common faults of the UAV sensor, namely the stuck fault and the constant deviation fault, are simulated and compared with the back propagation (BP) neural network model represented by the shallow neural network to verify the effectiveness of the proposed method in the paper.

Comprehensive Fault Diagnosis and Fault-Tolerant Protection of In-Vehicle Intelligent Electric Power Supply Network

For current power supply systems in internal combustion engine (ICE) vehicles, there exists a lack of comprehensive monitoring and sufficient management. There is no effective fault diagnosis and fault-tolerant protection either. With increasing number of devices applied in ICE vehicles, power supply system of vehicle is in desperate need of improvement. In this paper, a novel in-vehicle intelligent electric power supply network with comprehensive on-board fault diagnosis and real-time fault-tolerant protection is proposed. First, the architecture of in-vehicle intelligent electric power supply network is established, making the power supply process of all devices smarter. Then, a hybrid-signal-based method is presented for on-board detection of multifarious faults, and over-temperature failure of wiring harness is detected based on the proposed real-time temperature evaluation model of wiring harness. Furthermore, a multilevel fault-tolerant protection method is proposed based on redundant design, to achieve power supply security and avoid false protection. Finally, tests are performed on the vehicle as well as test bench to verify the feasibility and reliability of the proposed power supply network. The results reveal that each device's power supply process is appropriately monitored, online detection of faults is successfully achieved, and the fault-tolerant method is able to realize remediation and protection in real time.

A Comprehensive Fault Diagnosis Method for Rolling Bearings Based on Refined Composite Multiscale Dispersion Entropy and Fast Ensemble Empirical Mode Decomposition.

This study presents a comprehensive fault diagnosis method for rolling bearings. The method includes two parts: the fault detection and the fault classification. In the stage of fault detection, a threshold based on refined composite multiscale dispersion entropy (RCMDE) at a local maximum scale is defined to judge the health state of rolling bearings. If the bearing is in fault, a generalized multi-scale feature extraction method is developed to fully extract fault information by combining fast ensemble empirical mode decomposition (FEEMD) and RCMDE. Firstly, the fault vibration signals are decomposed into a set of intrinsic mode functions (IMFs) by FEEMD. Secondly, the RCMDE value of multiple IMFs is calculated to generate a candidate feature pool. Then, the maximum-relevance and minimum-redundancy (mRMR) approach is employed to select the sensitive features from the candidate feature pool to construct the final feature vectors, and the final feature vectors are fed into random forest (RF) classifier to identify different fault working conditions. Finally, experiments and comparative research are carried out to verify the performance of the proposed method. The results show that the proposed method can detect faults effectively. Meanwhile, it has a more robust and excellent ability to identify different fault types and severity compared with other conventional approaches.

Investigation on the failure mechanism and safety mechanical-thermal boundary of a multi-disc clutch

The comprehensive fault diagnosis method has been developed to analyze the failure of the multi-disc clutch. The mechanical buckling model is developed to investigate the relationship between the resistance torque and the periodic circumferential bright spots. The contact pressure and temperature on the friction surfaces are obtained with the consideration of the concentrated reactive load of the circlip. The friction and wear characteristics of the friction disc under different temperature conditions are explored by the pin-on-disc test, and then the surface morphologies of tested discs are observed by the metalloscope. The results demonstrate that both the thermal buckling and the mechanical buckling can result in the mechanical-thermal buckling. As the temperature varies from 15 °C to 400 °C, the disc experiences the abrasive wear, ploughing wear, adhesive wear and delamination wear in order. Finally, the mechanical-thermal safety boundaries are proposed. Moreover, the paper provides some suggestions for better design of clutch: the circlip should be optimized to avoid stress concentration; the teeth number of separate plates should be increased to improve the torque transmission capacity; the thickness of separate plates should be different which can be determined by their axial positions in the clutch pack.

基于组合分类器的MSF发动机综合故障诊断研究

基于组合分类器的MSF发动机综合故障诊断研究

Fuzzy Logic System for Intermixed Biogas and Photovoltaics Measurement and Control

This study develops a new integrated measurement and control system for intermixed biogas and photovoltaic systems to achieve safe and optimal energy usage. Literature and field studies show that existing control methods on small- to medium-scale systems fall short of comprehensive system optimization and fault diagnosis, hence the need to revisit these control methods. The control strategy developed in this study is intelligent as it is wholly based on fuzzy logic algorithms. Fuzzy logic controllers due to their superior nonlinear problem solving capabilities to classical controllers considerably simplify controller design. The mathematical models that define classical controllers are difficult or impossible to realize in biogas and photovoltaic generation process. A microcontroller centered fuzzy logic measurement and control embedded system is designed and developed on the existing hybrid biogas and photovoltaic installations. The designed system is able to accurately predict digester stability, quantify biogas output, and carry out biogas fault detection and control. Optimized battery charging and photovoltaic fault detection and control are also successfully implemented. The system is able to optimize the operation and performance of biogas and photovoltaic energy generation.

Design of an expert system for the prediction and comprehensive diagnosis of CNC machining fault

Unlike the earlier research works which only focused on fault diagnosis, in this paper a smart fault preventive step is taken in avoiding some category of failures which may be predicted at the initial stage of CNC operation before a fault is diagnosed. Besides this, the domain of fault diagnosis has also been expanded by incorporating human error which was not part of the earlier system. This is the first phase of the study and a comprehensive fault diagnosis expert system algorithm for CNC machine is going to be designed, which can later on be transformed into a knowledge-based intelligent computer applications program. This will be accomplished by consulting experts on various aspects of CNC operations.

An availability of MEMS-based accelerometers and current sensors in machinery fault diagnosis

Abstract In recent years, micro-electromechanical systems (MEMS)-based sensors have shown huge attraction in machinery fault diagnosis due to their low power consumption, low cost, small size, mobility, and flexibility. Hence, this paper presents a comprehensive fault diagnosis scheme using MEMS-based accelerometers and current sensors to identify several induction motor failures. In this paper, we first verify the reliability of these MEMS-based sensors via frequency analysis for vibration and current signals captured by them. Likewise, this paper validates their suitability for machinery fault diagnosis. To do this, we configure a 147-dimensional feature vector using statistical values (i.e., 21 statistical values × 7 MEMS-based accelerometers and current sensors), analyze fault signatures by employing a kernel principal component analysis, and pinpoint types of induction motor failures with one-against-all multi-class support vector machines (OAA MCSVMs), a random forest (RF), and a fuzzy k-nearest neighbor (Fk-NN). Experimental results indicate that the presented fault diagnosis approach using MEMS-based accelerometers and current sensors yields 100%, 86%, and 80% of classification accuracy with OAA MCSVMs, the RF, and the Fk-NN, respectively. Accordingly, MEMS-based sensors are enough for substituting commercial accelerometers and current sensors that are used for fault diagnosis. Specifically, MEMS-based accelerometers are far more effective for preserving intrinsic information about various induction motor failures than MEMS-based current sensors, offering at least 38% performance improvement in classification accuracy.

Research on a New Type of Self-Propelled Artillery Control System Test Equipment

This paper regard the self-propelled artillery control system as the research object, by using the adaptive control, data fusion and dual-mode modulation technology, construction of the hardware platform of the system is made, the fault diagnosis model and repair information management system is established, finally a corresponding test equipment is developed. the device has strong Integrity , good versatility, high stability, realize offline detection, parts of the joint debugging and commissioning and quality evaluation, the comprehensive fault diagnosis, repair decision-making and information management, greatly improve the force protection equipment repair capacity.

Diagnosis for uncertain, dynamic and hybrid domains using Bayesian networks and arithmetic circuits

System failures, for example in electrical power systems, can have catastrophic impact on human life and high-cost missions. Due to an electrical fire in Swissair flight 111 on September 2, 1998, all 229 passengers and crew on board sadly lost their lives. A battery failure most likely took place on the Mars Global Surveyor, which unfortunately last communicated with Earth and thus ended its mission on November 2, 2006. Fault diagnosis techniques that seek to hinder similar accidents in the future are being developed in this article. We present comprehensive fault diagnosis methods for dynamic and hybrid domains with uncertainty, and validate them using electrical power system data. Our approach relies on the use of Bayesian networks, which model the electrical power system, compiled to arithmetic circuits. We handle in an integrated way varying fault dynamics (both persistent and intermittent faults), fault progression (both abrupt and drift faults), and fault behavior cardinality (both discrete and continuous behaviors). Our work has resulted in a software system for fault diagnosis, ProDiagnose, that has been the top performer in three of the four international diagnostics competitions in which it participated. In this paper we comprehensively present our methods as well as novel and extensive experimental results on data from a NASA electrical power system.

Gear Fault Diagnosis Method Using Ensemble Empirical Mode Decomposition Energy Distribution and Grey Similar Incidence

Focusing on some correspondence between energy distribution of different frequency bands and fault types when gear fault occurs, a new comprehensive fault diagnosis method is proposed based on ensemble empirical mode decomposition and grey similar incidence. The idea of circle statistics is introduced to improve the shortcoming of traditional morphological filter; and the rank-order morphological filter is defined. The line structure element is selected for rank-order morphological filter to denoise the original acceleration vibration signal. Denoised vibration signals are decomposed into a finite number of stationary intrinsic mode functions and some containing the most dominant fault information are calculated the energy distribution. Due to the grey similar incidence has good classify capacity for small sample pattern identification; these energy distributions could serve as the feature vectors, the grey incidence of different gear vibration signals is calculated to identify the fault pattern and condition. Practical results show that the proposed method can be used in gear fault diagnosis effectively.

Design of the Fault Diagnosis and Maintenance Support System of New Self-Propelled Artillery Electromechanical Equipment

As the research object based on a variety of models of self-propelled artillery electromechanical equipment, detailed analysis was carried out on the self-propelled guns electrical equipment failure mode by using the artificial intelligence diagnosis and data mining technology, and a comprehensive, universal and high stability fault diagnosis and maintenance support system for the new self-propelled artillery electromechanical equipment is developed, which realize offline testing, FBI test and quality assessment, a combination of fault diagnosis, maintenance decision-making and information management. Based on the system hardware platform, the fault diagnosis model and maintenance information management system are established, the corresponding fault diagnosis and maintenance support system are finally developed.By passing test, the system have strong comprehensive ,good versatility and the characteristics of high stability.

Application of EEMD Energy Distribution and Grey Incidence in Gearbox Fault Diagnosis

In this paper, a new comprehensive gearbox fault diagnosis method was proposed based on rank-order morphological filter, ensemble empirical mode decomposition (EEMD) and grey incidence. Firstly, the rank-order morphological filter was defined and the line structure element was selected for rank-order morphological filter to de-noise the original acceleration vibration signal. Secondly, de-noised gearbox vibration signals were decomposed into a finite number of stationary intrinsic mode functions (IMF) and some IMFs containing the most dominant fault information were calculated the energy distribution. Finally, due to the grey incidence has good classify capacity for small sample pattern identification; these energy distributions could serve as the feature vectors, the grey incidence of different gearbox vibration signals was calculated to identify the fault pattern and condition. Practical results show that the proposed method can be used in gear fault diagnosis effectively.

Research on Comprehensive Fault Diagnosis Method of Air Compressor Oil Based on Intelligent Multi-objective Optimization

Research on Comprehensive Fault Diagnosis Method of Air Compressor Oil Based on Intelligent Multi-objective Optimization