Proposed Diagnosis Approach Research Articles

A minor-fault diagnosis approach based on modified variance for lithium-ion battery strings

In order to prevent battery accidents of electric vehicles (EVs), it is significant to quickly diagnose and recognize faults of lithium-ion battery strings. Nevertheless, the diagnosis of initial minor faults is an intractable problem because the minor fault voltages often do not trigger the cut-off voltage and are hidden in the normal voltage sequences which pose a great threat to the safe operation of electric vehicles. Therefore, this paper proposes a simple real-time minor fault diagnosis approach based on modified variance. By calculating the modified variance of the battery voltage sequences in a sliding-window, the proposed diagnosis approach is able to effectively discern the type and the time of minor battery abnormities, which comprises open circuit and short circuit faults. The purpose of the sliding-window is to take a sliding reading of the voltage sequence in a window with appropriate length in order to improve the accuracy of battery fault diagnosis. Then, the experimental results and the contrast with the previously existing approaches verify the feasibility of the proposed method with low computational cost, easy realization, and without the battery model. Particularly, it can be used to effectively diagnose the minor faults of the battery under different ambient temperatures because of strong robustness and sensitivity. In conclusion, the proposed minor fault diagnosis method has practicable applications in electric vehicles.

Fault diagnosis of driving gear in rack and pinion drives based on multi-scale local binary pattern extraction and sparse representation

Rack and pinion drives (RPDs) are key components of battery-swapping systems (BSSs) used in electric heavy trucks; the faults occurring in these drives reduce the efficiency, accuracy, quality of battery swapping, and even pose potential safety risks. The operating conditions of RPD driving gear in BSSs are characterized by speed fluctuations, relatively low speeds, and reciprocating motion. To assess the driving gear fault characteristics under these conditions, based on the solution of image recognition under complex and low illumination conditions, this study proposes a fault diagnosis framework that includes adaptive down-sampling, three-dimensional acceleration data fusion, multi-scale local binary pattern (MS-LBP) extraction, and sparse representation. First, adaptive down-sampling is used to smooth out the speed fluctuation. Subsequently, MS-LBP extraction is employed to obtain efficient fault features at low speed. Finally, dictionary learning and sparse representations are conducted on the MS-LBP features. The effectiveness and advantages of the proposed diagnosis approach are demonstrated using monitoring data acquired from a BSS. Moreover, comparative studies demonstrate that the proposed fault diagnosis method yields improved performance.

A Novel Generic Diagnosis Algorithm in the Time Domain Representation

The health monitoring of a system remains a major issue for its lifetime preservation. In this paper, a novel fault diagnosis algorithm is proposed. The proposed diagnosis approach is based on a unique variable measurement in the time domain and manages to extract the system behavior evolution. The developed tool aims to be generic to several physical systems with low or high dynamic behavior. The algorithm is depicted in the present paper and two different applications are considered. The performance of the novel proposed approach is experimentally evaluated on a fan considering two different faulty conditions and on a proton exchange membrane fuel cell. The experimental results demonstrated the high efficiency of the proposed diagnosis tool. Indeed, the algorithm can discriminate the two faulty operation modes of the fan from a normal condition and also manages to identify the current system state of health. Regarding the fuel cell state of health, only two conditions are tested and the algorithm is able to detect the fault occurrence from a normal operating mode. Moreover, the very low computational cost of the proposed diagnosis tool makes it especially suitable to be implemented on a microcontroller.

A novel fault diagnosis scheme for a ballast system

The Back Propagation (BP) neural network model is one of the neural network models suitable for model recognition and classification. However, a single BP network model cannot fully tap the feature information hiding in the data and is vulnerable to the uncertainty associated with system parameter changes and also suffers from the effects of long diagnosis times, inaccurate and undesired diagnosis results in applying a large amount of high-dimensional feature data. In addition, the standard BP algorithm has limitations in pattern recognition and classification. For example, it adopts the quadratic cost function to train the neural network, which can lead to low convergence accuracy, low convergence rate, and so on. Compared with the quadratic cost function, the cross-entropy cost function can effectively improve the convergence accuracy and the convergence rate of the neural network. The Dempster–Shafer (D–S) evidence theory is a powerful tool for handling incomplete, imprecise, and uncertain information and merging it when it derives from multiple BP networks. In order to overcome the drawbacks of a single BP network, it is necessary to divide the high-dimensional feature data into several different categories of low-dimensional feature data and to input the same dimensional feature data into the same BP network model, as well as to design the information fusion algorithms based on D–S evidence theory and multiple improved BP networks with the cross-entropy cost function. For this reason, this paper proposes a novel information fusion diagnosis approach based on D–S evidence theory and multiple improved BP networks for fault diagnosis and classification of multiple devices in a ballast system subjected to extreme environmental conditions. The proposed diagnosis approach utilizes multiple neural networks to carry out local fault diagnosis, adopts the D–S evidence theory to fuse the results of local diagnosis and derive the decision results, and introduces an improved BP algorithm based on the cross-entropy cost function into the individual neural networks to enhance the convergence rate and diagnosis accuracy of the individual neural networks. This method can overcome the deficiencies of individual BP networks in multiple BP networks in their poor diagnostic ability to handle incomplete, imprecise, and uncertain sample information. According to the design idea of the proposed diagnosis approach, a new fusion modeling for fault diagnosis of multiple devices is constructed for a ballast system. The validity of the proposed diagnosis method and model is illustrated through both simulation studies and experimental tests.

Intelligent fault diagnosis of bevel gearboxes using semi-supervised probability support matrix machine and infrared imaging

Fault diagnosis is of great significance to ensure the reliability and safety of complex bevel gearbox systems. Most existing intelligent fault diagnosis approaches of bevel gearboxes are designed with vibration monitoring. However, the collected vibration data are vulnerable to noise pollution and machinery operating conditions. Besides, traditional fault diagnosis models highly rely on numerous labeled samples, and neglect the high cost of label annotation in real-world applications. Therefore, a novel fault diagnosis approach based on semi-supervised probability support matrix machine (SPSMM) and infrared imaging is proposed for bevel gearboxes in this paper, which has the following properties. Firstly, SPSMM classifies 2D matrix data directly without vectorization, thus fully utilizing the spatial information in infrared images. Secondly, a probability output strategy is designed for SPSMM to calculate the posterior class probability estimation of matrix inputs, and consequently enhance the diagnostic accuracy and robustness of the model. Thirdly, a semi-supervised learning (SSL) framework is proposed for SPSMM to carry out sample transfer from the unlabeled sample pool to the labeled sample pool, which can effectively alleviate the problem of insufficient labeled samples. The superiority of the proposed diagnosis approach is demonstrated with an infrared imaging dataset of a bevel gearbox.

An Enhanced Pathfinder Algorithm based MCSA for rotor breakage detection of induction motor

This paper proposes a new broken rotor bar (BRB) finding technique based on a novel hybrid algorithm, namely the Enhanced Pathfinder Algorithm (EPFA). Compared to existing optimization algorithms, this proposed approach provides more balanced exploration and exploitation. The main task regarding rotor fault detection is to identify sideband frequency components (SFCs) corresponding to the fault and in this context proposed method overcomes the disadvantages of traditional frequency domain analysis, such as short data length, effects of spectral leakage, and insufficient sampling frequency. To achieve the proposed diagnosis approach, a signal model for faulty current and a function of residual error are used for the formulation of a nonlinear least-square problem. The method is first tested on a synthetic fault signal, and finally, to validate the proposed technique, a Finite Element Model (FEM) of the induction motor is simulated in the ANSYS 2D workbench.

Diagnosis for Reconfigurable Single-Electron Transistor Arrays with a More Generalized Defect Model

Singe-Electron Transistor (SET) is considered as a promising candidate of low-power devices for replacement or co-existence with Complementary Metal-Oxide-Semiconductor (CMOS) transistors/circuits. In this work, we propose a diagnosis approach for SET array under a more generalized defect model. With the more generalized defect model, the diagnosis approach will become more practical but complicated. We conducted experiments on a set of SET arrays with different dimensions and defect rates. The experimental results show that our approach only has 3.8% false-negative rate and 0.7% misjudged-category rate on average without reporting any false-positive edge when the defect rate is 4%. Therefore, the proposed diagnosis approach can diagnose the defective SET arrays and elevate the reliability of the SET arrays in the synthesis flow.

Diagnosis of brushless synchronous generator using numerical modeling

PurposeOn-time fault diagnosis in electrical machines is a critical issue, as it can prevent the development of fault and also reduce the repairing time and cost. In brushless synchronous generators, the significance of the fault diagnosis is even more because they are widely used to generate electrical power all around the world. Therefore, this study aims to propose a fault detection approach for the brushless synchronous generator. In this approach, a novel extension of Relief feature selection method is developed.Design/methodology/approachIn this paper, by taking the advantages of the finite element method (FEM), a brushless synchronous machine is modeled to evaluate the machine performance under two conditions. These conditions include the normal condition of the machine and one diode open-circuit of the rotating rectifier. Therefore, the harmonic behavior of the terminal voltage of the machine is obtained under these situations. Then, the harmonic components are ranked by using the extension of Relief to extract the most appropriate components for fault detection. Therefore, a fault detection approach is proposed based on the ranked harmonic components and support vector machine classifier.FindingsThe proposed diagnosis approach is verified by using an experimental test. Results show that by this approach open-circuit fault on the diode rectifier can effectively be detected by the accuracy of 98.5% and by using five harmonic components of the terminal voltage [1].Originality/valueIn this paper, a novel feature selection method is proposed to select the most effective FFT components based on an extension of Relief method, and besides, FEM modeling of a brushless synchronous generator for normal and one diode open-circuit fault.

Exponential random graph modeling for diagnosis of out‐of‐control signals in social network surveillance: Signal interpretation in social networks with ERG modeling

AbstractMonitoring of the social networks for detecting anomalous behavior could be vital for the system's survival. This anomalous behavior could raise from any changes in behavior or attributes of a particular individual or groups of individuals in the network and causes structural changes. Multivariate statistical process control charts are effective tools for this purpose while Exponential Random Graph Models are used to model highly interdependent data of the network. So after selecting a model for specific network, T2 control charts are used for monitoring the network data to detect any anomalous behavior. Then the Mason, Tracy, and Young method is utilized for interpreting an out‐of‐control condition. Finally, some real‐world examples are used to evaluate the performance of the proposed diagnosis approach. Since complicated dependency in a social network makes different choices in model selection for Exponential Random Graph Models and this causes various results in the evaluation study, if the impact of diagnosis result is not seen in model selection, the appropriate model will not be necessarily selected and this will affect the effectiveness of the whole system. So, in this paper for improving the performance of diagnosis, two indices are introduced and added to model selection criteria and then the appropriate model could be selected based on the decision‐maker's preferences.

Fault diagnosis for hybrid systems based on a bank of linear observers and a discrete automaton

This paper presents a fault diagnosis approach for a four-tank system considered as a hybrid dynamical system. The proposed diagnosis approach is based on a hybrid observer composed of a discrete automaton and a bank of linear observers. An automaton is used to improve the performance of the proposed method. For this study, we considered sensor, parametric and discrete faults. Simulation results showed the efficiency of the proposed diagnosis approach to detect and to localize the considered faults, despite the hybrid behaviors of the studied system.

A Chaotic Feature Extraction Based on SMMF and CMMFD for Early Fault Diagnosis of Rolling Bearing

Various failure mechanisms of rolling bearing under different working conditions involve important nonlinear dynamic characteristics. And the incipient fault detection of the complex and non-stationary rolling bearing signal is difficult, especially with multiple interference source components. To address this issue, a novel fault feature extraction method which integrates chaos theory and mathematical morphology is proposed. Firstly, the smooth multi-scale morphological filtering (SMMF) is adopted to reduce the noise in chaotic rolling bearing signals without destroying its original nonlinear dynamic structure. In addition, a method of generalized estimation of morphological fractal dimension called composite multi-scale morphological fractal dimension (CMMFD) is proposed, and applied to quantify the complexity of those orbits reconstructed from rolling bearing signals. The CMMFD improves the identification ability of the original morphological fractal dimension (MFD). Then, CMMFD features are extracted from SMMF denoised signal. Different conditions of rolling bearing signals are distinguished in this feature space. Finally, the CMMFD features are input into a Hidden Markov Model (HMM) training and diagnosis to distinguish different faults of rolling bearing signals. The new diagnosis approach based on SMMF and CMMFD is compared with some existing methods in analysis of simulated signals and experimental rolling bearing signals. Accuracy and efficiency of different approaches are contrasted by analyzing the results of simulation and experiments. Comparative results demonstrate that the proposed diagnosis approach based on SMMF and CMMFD can reliably recognize different fault types at the early stage and have more accurate results.

Generalized Likelihood Ratio Test Based Approach for Stator-Fault Detection in a PWM Inverter-Fed Induction Motor Drive

This paper proposes a new approach for the detection of stator faults in inverter-fed induction motor under closed-loop control. These faults, generally, start with interturn short circuit and can evolve to phase-to-phase or phase-to-ground faults, leading to stator currents unbalance. In the considered application, the stator windings are supplied by a static converter for the control of the speed and the rotor flux of the motor. The unbalance fault has been artificially created through additional resistance in series with one phase of the stator windings. The proposed diagnosis approach is based on the computation of the symmetrical components of the stator currents. The supply fundamental frequency and the three-phase phasors are estimated based on the maximum likelihood estimator. Then, the generalized likelihood ratio test is applied for unbalance fault detection. Simulation and experimental results on a 1.5-kW induction motor illustrate the effectiveness of the proposed approach, leading to an effective diagnosis procedure for stator faults in inverter-fed induction motor under steady state and closed-loop operation.

Wind turbine power converter fault diagnosis using DC-link voltage time–frequency analysis

With the developments in power electronic devices, there is increasing use of the insulated gate bipolar transistor devices in power converters. Power converters are more and more gaining attention in the wind energy conversion system. In this article, a grid-connected wind energy conversion system is considered. Wind energy conversion system optimal operation requires a diagnostic method for back-to-back power converter to be addressed in detail in this article. Therefore, an open-circuit switching fault diagnosis method for a back-to-back power converter is developed to improve the system reliability and optimize the produced power and also achieve its real operational cost. In this work, we investigate only the DC-Link voltage signal and we use the time–frequency analysis to achieve the diagnosis purpose. The scheduled diagnosis approach is divided into two tasks: the first one is the fault detection task and the second one is the fault localization task. The main novelty of the proposed diagnosis approach is to localize the appropriate faulty power converter, that is, rotor side power converter or grid side power converter. Also, this approach is able to localize faults when there is a simultaneous fault on both power converters. Simulations are carried out to verify the robustness of the proposed diagnosis approach. The achieved simulation results would help in diagnosing of Back-to-back power converter that would expectedly cancel the traditional diagnosis method.

Diagnosis of the Induction Machine Using Advanced Signal Processing Methods

This work is a part of the thematic of monitoring and fault diagnosis of the squirrel cage three-phase induction machine. The choice of this type of machine is justified by the growing success it has exhibited, mainly, in the electric drives with variable speed. Signal based detection methods are presented is validated in simulation. The proposed diagnosis approach requires only little experimental data, and more importantly it provides efficient simulation tools that allow characterizing faulty behavior.In this study, the proposed approach considers the value of rotor resistance as fixed for condition monitoring. This value in the diagnostic tools which one uses is not fixed contrary to the classical approaches of control of machine.

Diagnosis of Partially Observed Petri Net Based on Analytical Redundancy Relationships

AbstractIn this paper, we design an efficient diagnosis technique for partially observed discrete event systems modeled by labeled Petri nets. The fault detection is based on analytical redundancy relationships derived from the nominal model. The decomposition of the Tun‐induced subnet to connected subgraphs allows determining the subgraphs that may contain faults. To appreciate the fault localization, a set of analytical redundancy relationships is etablished for each fault transition based on the fault model. The proposed diagnosis approach is independent of the length of the observed sequence and independent of the number of unobservable transitions. The detected faults with the proposed approach are faults which led to a change in the number of tokens in the net.

Diagnosis of sucker rod pumping based on dynamometer card decomposition and hidden Markov model

In oil field production, dynamometer card is the key source of information to analyze the down-hole operating conditions of sucker rod pumping. However, under different operating conditions, most of the existing diagnostic technologies are incapable to extract features from dynamometer cards automatically and comprehensively. Based on the mechanism analysis of dynamometer card, a useful diagnostic method with novel feature extraction method is proposed for diagnosing the operating condition of sucker rod pumping. A novel barycentric decomposition strategy is applied to divide the dynamometer cards, which can automatically adjust divided regions according to the shape change of dynamometer card. The curve moments are extracted from the parts of divided results to obtain the comprehensive features for the follow-up algorithm. Subsequently, the hidden Markov model with mixture density function is designed as a classifier to map the relationship between the operating condition and the features of dynamometer card. This technique is successfully carried out in a fault dynamometer card atlas that is collected from productive field. Finally, the experimental results confirm the validity of the proposed diagnosis approach.

A method for identifying faulty cells using a classification tree-based UE diagnosis in LTE

The latest advances in wireless technologies have led to a proliferation of data mobile devices and services. As a consequence, mobile networks have experienced a significant increase in data traffic, while voice traffic has shown nearly no growth. It is therefore essential for operators to understand the data traffic behavior at the user level in order to ensure a good customer experience. In the radio access networks (RANs), traditional solutions based on cell-level measurements are not adequate to analyze performance of individual users. Instead, novel alternatives such as the use of call traces and the definition of new user-centric indicators will provide detailed and valuable information for each connection. One of the key measurements related to data services is the user throughput. In this work, the user throughput is adopted as the main attribute to conduct diagnosis in the RAN, which has typically been the bottleneck for data services. To that end, a binary classification tree is proposed to determine the root cause of poor throughput in user-level data sessions. Then, this information is aggregated at the cell level in order to provide effective diagnosis of degraded cells. In particular, a correlation-based analysis of the cell status is proposed in order to identify abnormal cell behaviors in an automatic way. Evaluation has been carried out with datasets from live cellular networks. Results show that the proposed diagnosis approach is an effective means to identify the main factors that limit the user throughput in network cells.

A Synthesized Diagnosis Approach for Lithium-ion Battery in Hybrid Electric Vehicle

A synthesized diagnosis approach for the lithium-ion battery is proposed with fully understanding of the internal failure mechanism, which is suitable for dynamic conditions such as hybrid electric vehicles. In order to detect and distinguish different fault modes and bridge external electrical parameters with internal chemical mechanisms, a serial of abusive experiments, including overcharge, over-discharge, and low-temperature operation, which commonly occur during battery applications, are arranged. Fault symptoms in the form of electrical parameter variation are extracted with reference performance tests, which consist of hybrid pulse power characteristic test, federal urban driving schedule test, and incremental capacity analysis. The proposed diagnosis approach not only indicates the occurrence of fault but provides a detailed description of symptoms and physical meaning for internal mechanisms as well.

Fault Detection and Isolation of spacecraft thrusters using an extended principal component analysis to interval data

This paper presents a new interval diagnosis method to detect and isolate actuators faults of an autonomous spacecraft involved in the rendez-vous phase of the Mars Sample Return (MSR) mission. The proposed diagnosis approach is based on the Vertices Principal Component Analysis (VPCA) as an extension of the classical PCA method to interval data. To ensure the feasibility of the proposed Fault Detection and Isolation (FDI) approach, a set of interval data provided by the MSR “high-fidelity” industrial simulator and representing the opening rates of the spacecraft thrusters has been considered. The results have proven the efficiency of the proposed FDI approach in the diagnosing process assuring the detection and the isolation of both single and multiple faults.

An Extender Kalman Filter-based Induction Machines Faults Detection

This paper deals with the detection and localization of electrical drives faults, especially those containing induction machines. First, the context of the study is presented and an Extended Kalman Filter is described for induction machines fault detection. Then the modeling procedure under faulty conditions is shown, and the machine diagnosis methods are developed. The proposed diagnosis approach requires only little experimental data, and more importantly it provides efficient simulation tools that allow characterizing faulty behavior.Fault detection uses signal processing techniques in known operating phases (fixed speed), considering and locating malfunctions.