Combined Fault Detection Research Articles

Rotating machinery weak fault features enhancement via line-defect phononic crystal sensing

The fault features of rotating machinery at the early degradation stage are always weak as a result of interference from strong noise and irrelevant harmonics. Although traditional acoustic diagnosis techniques have attracted much attention with the merits of rich information and non-contact, extracting meaningful features related to faults in extremely low signal-to-noise ratios (SNRs) has always been a challenging problem. Considering the extraordinary physical properties of phononic crystals (PnCs) and acoustic metamaterials, this study proposes a structural enhancement method for rotating machinery fault diagnosis via line-defect PnC sensing. In contrast to conventional acoustic filtering and enhancement methods, this method can directly filter out noise and enhance fault features in the pre-processing stage of acoustic perception without the need for complex post-processing algorithms. Consequently, the original information of the fault features is preserved intact, thus further increasing the detection limit of current acoustic sensing. Specially, the designed line-defect PnC is parametrically tunable. Combined with the prior knowledge of rotating machinery fault features, such as gears and bearings, it is possible to design structures suitable for enhancing their fault features, which has great potential for practical engineering applications. The enhancement mechanism of line-defect PnC is theoretically described, and numerical simulations are also conducted to verify its ability to detect weak faults in rotating machinery. The experimental results show that by comparison with the variational modal decomposition (VMD)-based method, the proposed method exhibits superior fault feature enhancement performance under low SNR conditions. By systematically combining fault detection methods and acoustic metamaterial sensing, it can be foreseen that the proposed method shows great potential in mechanical equipment fault diagnosis.

Gearbox mixed faults diagnosis under different working conditions based on meshing frequency modulation area

Condition monitoring of gearboxes is mainly based on vibration analysis. Traditionally, spectral lines related to shaft speeds and gear meshing frequency (GMF) are the key components that serve as fault indicators. However, due to the noise level and the dependence of signal energy on operating conditions, this technique can fail. In this article, we present an alternative technique based on the calculation of the area covered by a spectral band around the GMF. This band is observed in the envelope spectrum of the vibration data and covers the modulating components characterizing the appearance of the defects. The experimental results obtained from a gearbox test bench operating under different speeds and load levels show the effectiveness of the proposed fault indicator in the detection of combined faults. Compared to certain other known methods for diagnosing gear faults, the proposed technique shows its superiority in terms of processing time and simplicity of implementation.

Fault-Tolerant Control Strategy for Sub-Modules Open-Circuit Fault of Modular Multilevel Converter

Modular multilevel converter (MMC) is a key device of high-voltage-direct circuit (HVDC) transmission system, the sub-module detection technology of which will directly influence the damage severity, and even the reliability of the whole system. In this paper, the open-circuit fault characteristics of an insulated gate bipolar transistor (IGBT) in a sub-module are analyzed, and a fault-tolerant optimal control strategy is proposed for the redundant hot-reserved MMC based on nearest-level modulation (NLM). A fault sub-module diagnosis and location strategy based on the deviation distance of the capacitor voltages is presented. After the faulty sub-module is removed, due to the asymmetric operation of the MMC, odd-order circulating currents are introduced in the faulty phase, in which the fundamental-frequency circulating current is the major component; the fundamental-frequency voltage related to the redundancy rate is injected into the faulty phase, which effectively suppresses the fundamental-frequency circulating current and harmonics in the faulty phase. The proposed method combines fault detection and fault ride-through steps, so it has the features of high reliability and high compatibility. Based on the Matlab/Simulink simulation model, the effectiveness of the proposed strategy is verified.

Cost-effective process experimental studies on stator inter-turn faults detection in induction motor using harmonic RPVM and decomposition wavelet transform

Abstract This paper presents the development of a new algorithm for the diagnosis of induction motor inter-turn short circuit and mixed combined fault detection based on the reduction Park’s vector modulus and discrete wavelet transform. The proposed method is based on a combination between the RPVM approach and DWT. This new signal of the RPVM is a cost-effective alternative implementation of the Extended Park’s Vector approach through monitoring only the fluctuations of the stator current zero-crossing times in the frequency domain. Using this technique, the characteristic frequency components of the fault are extracted from only one phase stator current when compared to Park’s vector modulus and Hilbert modulus. The main advantages of the RPVM signal are to acquire the values of a single-phase stator current to allow reducing the calculation complexity and for extracting features frequencies of the faults. Thus, the reduced amount of data samples must be stored and processed. This study investigates theoretically by using the proposed RPVM signal for performing to diagnose induction motor faults through the DWT method and processed via energy eigenvalue of energies. This proposed approach RPVM-DWT is validated experimentally to demonstrate its effectiveness in that the proposed algorithm reduces the computational cost and can be used in induction motor fault detections and identifications with different fault severity conditions.

Advances in Fault Condition Monitoring for Solar Photovoltaic and Wind Turbine Energy Generation: A Review

Renewable energy-based power generation technologies are becoming more and more popular since they represent alternative solutions to the recent economic and environmental problems that modern society is facing. In this sense, the most widely spread applications for renewable energy generation are the solar photovoltaic and wind generation. Once installed, typically outside, the wind generators and photovoltaic panels suffer the environmental effects due to the weather conditions in the geographical location where they are placed. This situation, along with the normal operation of the systems, cause failures in their components, and on some occasions such problems could be difficult to identify and hence to fix. Thus, there are generated energy production stops bringing as consequence economical losses for investors. Therefore, it is important to develop strategies, schemes, and techniques that allow to perform a proper identification of faults in systems that introduce renewable generation, keeping energy production. In this work, an analysis of the most common faults that appear in wind and photovoltaic generation systems is presented. Moreover, the main techniques and strategies developed for the identification of such faults are discussed in order to address the advantages, drawbacks, and trends in the field of detection and classification of specific and combined faults. Due to the role played by wind and photovoltaic generation, this work aims to serve as a guide to properly select a monitoring strategy for a more reliable and efficient power grid. Additionally, this work will propose some prospective with views toward the existing areas of opportunity, e.g., system improvements, lacks in the fault detection, and tendency techniques that could be useful in solving them.

Closed-Loop Drive Detection and Diagnosis of Multiple Combined Faults in Induction Motor Through Model-Based and Neuro-Fuzzy Network Techniques

In this paper, a fault detection and diagnosis approach adopted for an input-output feedback linearization (IOFL) control of induction motor (IM) drive is proposed. This approach has been employed to detect and identify the simple and mixed broken rotor bars and static air-gap eccentricity faults right from the start its operation by utilizing advanced techniques. Therefore, two techniques are applied: the model-based strategy, which is an online method used to generate residual stator current signal in order to indicate the presence of possible failures by means of the sliding mode observer (SMO) in the closed-loop drive. However, this strategy is not able to recognise the fault types and it can be affected by the other disturbances. Therefore, the offline method using the multi-adaptive neuro-fuzzy inference system (MANAFIS) technique is proposed to identify the faults and distinguish them. However, the MANAFIS required a relevant database to achieve satisfactory results. Hence, the stator current analysis based on the HFFT combination of the Hilbert transform (HT) and Fast Fourier transform (FFT) is applied to extract the amplitude of harmonics due to defects occur and used them as an input data set for the MANFIS under different loads and fault severities. The simulation results show the efficiency of the proposed techniques and its ability to detect and diagnose any minor faults in a closed-loop drive of IM.

Fault detection and diagnosis with a novel source-aware autoencoder and deep residual neural network

The capability of deep learning (DL) techniques for dealing with non-linear, dynamic and correlated data has paved the way for DL-based fault detection and diagnosis (FDD). Among them, autoencoders (AEs) have shown their potential to serve as the fault detection network. However, misclassifying faulty samples that share similar patterns to normal samples is a common drawback of AEs. In this work, a source-aware autoencoder (SAAE) is proposed as an extension of AEs to incorporate faulty samples in the training stage. In SAAE, flexibility in tuning recall and precision trade-off, ability to detect unseen faults and applicability in imbalanced data sets are achieved. Bidirectional long short-term memory (BiLSTM) with skip connections SAAE is designed as the structure of the fault detection network. Further, a deep network with BiLSTM and residual neural network (ResNet) is proposed for the subsequent fault diagnosis step to avoid randomness imposed by the order of the input features. A framework for combining fault detection and fault diagnosis networks is also presented without the assumption of having a perfect fault detection network. A comprehensive comparison among relevant existing techniques in the literature and SAAE-ResNet is also conducted on the Tennessee-Eastman process, which shows the superiority of the proposed FDD method.

Co-Design of Fault Detection and Consensus Control Protocol for Multi-Agent Systems Under Hidden DoS Attack

This article is concerned with the co-design of fault detection and consensus control protocol for a class of multi-agent systems (MASs) subject to Denial-of-Service attack, where the attack behavior is hidden to the defender as the adversary may launch the attack with different durations but the defender may not know the real situation at each time instant. Due to the complicated attack behavior, a hidden semi-Markov process is introduced where only the observed attack modes emitted by the inaccessible ones are available. The probability density functions of sojourn time and the semi-Markov kernel are adopted to help analyze the combined fault detection and consensus control system. Sufficient conditions on the σ-error mean square stability of MASs with mixed H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> /H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-</sub> performance are established by using a set of Lyapunov functions that depend on the hidden and the observed attack modes. Moreover, the gain matrices of controller and detector are obtained by solving an optimization problem with some matrix inequality constraints. Finally, the simulation of autonomous unmanned underwater vehicles is used to show the effectiveness of the proposed results.

Condition Assessment of I&amp;C Cables in Nuclear Power Plants via Stepped-Frequency Waveform Reflectometry

A nuclear power plant (NPP) depends on instrumentation and control (I&C) systems to ensure its safe and efficient operation. In particular, I&C cables take on the pivotal role of measuring and controlling the critical equipment of the NPP. Thus, it is indubitable that the diagnostic technology of I&C cables for detecting faults and accurately assessing their health status is required for ensuring the safety and reliability of the NPP operation. We propose a diagnostic method that combines fault detection and evaluation algorithm for the I&C cables with stepped-frequency waveform reflectometry with signal propagation and reflection modeling. The signal modeling allows the assessment of the fault with an estimated reflection coefficient by separating the propagation and reflection effects of the measured signal. In short, cable faults are differentiated and quantified regardless of distance. The proposed algorithm is verified by characteristic impedance measurement, various fault detection/evaluation experiments, and the fault evaluation of local accelerated thermal aging cable.

A Novel Rail Inspection Robot and Fault Detection Method for the Coal Mine Hoisting System

Rail is an important part of coal mine hoisting system, and its fault detection is an important work of mine daily maintenance. In this paper, a new inspection robot system and corresponding detection method are proposed to detect the faults in the rail of coal mine hoisting system. The robot system will replace the manual inspection work in the mine, and will greatly improve the efficiency of inspection. The robot is driven by a magnetic wheel to ensure its smooth motion on a vertical rail. In order to increase the adsorption capacity of the magnetic wheel, the halbach array based on multi block magnets is adopted in the magnetic wheel, and the relationship between the number of magnets and the adsorption force is analyzed. In addition, aiming at the problem of rail fault detection, a new method is proposed to estimate the rail deformation by using the robot's attitude perception. A method of robot positioning on rail is also proposed by combining fault detection as landmark and mileage estimation. Finally, the proposed method is verified by experiments. This inspection robot system can improve the efficiency of rail inspection of mine hoisting system, and the proposed detection scheme can also be extended to railway rail inspection.

Effect Evaluation of Fault Resistance on the Operating Behavior of a Distance Relay

This paper presents an application of a certain distance protection relay with a quadrilateral characteristic approach for the protection of the 110kV Duy Xuyen - Thang Binh transmission line in Vietnam using measured data from one terminal line. We propose the building process of a Matlab Simulink model for this relay that combines fault detection and classification block, apparent impedance calculation block for all types of faults and a trip logic block of three zone protection coordination. The proposed relay model is further tested using various fault scenarios on the transmission line. It is important to assess what happened, the actual conditions, the causes of mal-operation etc. Detailed explanation and results indicate that the proposed model behavior will help users to perform tests which correctly simulate real-world conditions besides that it properly interprets test results and troubleshoot distance function problems when results are not as expected.

Non-Mutually Exclusive Deep Neural Network Classifier for Combined Modes of Bearing Fault Diagnosis.

The simultaneous occurrence of various types of defects in bearings makes their diagnosis more challenging owing to the resultant complexity of the constituent parts of the acoustic emission (AE) signals. To address this issue, a new approach is proposed in this paper for the detection of multiple combined faults in bearings. The proposed methodology uses a deep neural network (DNN) architecture to effectively diagnose the combined defects. The DNN structure is based on the stacked denoising autoencoder non-mutually exclusive classifier (NMEC) method for combined modes. The NMEC-DNN is trained using data for a single fault and it classifies both single faults and multiple combined faults. The results of experiments conducted on AE data collected through an experimental test-bed demonstrate that the DNN achieves good classification performance with a maximum accuracy of 95%. The proposed method is compared with a multi-class classifier based on support vector machines (SVMs). The NMEC-DNN yields better diagnostic performance in comparison to the multi-class classifier based on SVM. The NMEC-DNN reduces the number of necessary data collections and improves the bearing fault diagnosis performance.

Diagnosis of the combined rotor faults using air gap magnetic flux density spectrum for an induction machine

This paper presents a method for the diagnosis of induction machines faults. The proposed method is capable to detect the presence of both dynamic eccentricity and broken rotor bar faults. Several studies have attempted to model an induction machine with isolated faults and provide methods for detecting them. However, the challenge begins, with the occurrence of combined defects which produce fault signatures that are difficult to separate. The novel proposed method is based on the measured air-gap magnetic flux density spectrum, which allows for the detection of combined faults. A finite element method is used for modelling the induction machine under faulty conditions, where the faults of rotor bars are created by a deleting operation of the boundary condition which is added to the air-gap part. Then, the dynamic eccentricity is formed by the movements of the rotating rotor’s centre with different ratings. From a modelling perspective, the contribution of the current work is the establishment of the relation of the air-gap of the rotor for modelling this kind of eccentricity fault. In addition, the proposed model of the air-gap includes two parts; one related to the stator and another one to the rotor, called statoric air-gap and rotoric air-gap respectively. The rotoric air-gap is employed for the dynamic eccentricity modelling. Computer simulations are presented using the air-gap magnetic vector and the magnetic field in X and Y components, to confirm the robustness of the proposed technique. Finally, the air-gap magnetic flux density spectrum is used for the analysis of combined rotor faults.

Fractal dimension-based approach for detection of multiple combined faults on induction motors

Induction motors, key elements for industry, are susceptible to one or more faults at the same time; yet, they can keep working without affecting the process, but increasing the production costs. For this reason, a monitoring system that can efficiently diagnose the induction motor condition, even under multiple combined faults, is a demanding task. In this work, a methodology and its implementation into a field programmable gate array for an online and real-time monitoring system of multiple combined faults are presented. First, the fractal dimension approach, using the Katz algorithm, is introduced as a measure of variation of 3-axis startup vibration signals for the induction motor condition, considering that these signals describe changes on its dynamic characteristics due to the different faults. Then, an artificial neural network determines in an automatic way the induction motor condition according to the fractal dimension values. The obtained results show a higher overall efficiency than previous works for detecting broken rotor bars, outer-race bearing defects, unbalance, and their combinations, as well as a healthy condition.

Chi-square and SPRT combined fault detection for multisensor navigation

A fault-detection algorithm for a redundant multisensor navigation system for hypersonic cruise vehicles (HCVs) is proposed. The algorithm comprehensively diagnoses failures according to the failure level monitored by the sequential probability ratio test (SPRT) and chi-square test as well as the failure trend monitored by the SPRT. A test statistics feedback-reset loop is also added to shorten the recovery time after failure ceases. Simulations indicate improvements in both failure detection and recovery speed, contributing to improved accuracy and stability in HCV fault-tolerant navigation.

Fused Empirical Mode Decomposition and MUSIC Algorithms for Detecting Multiple Combined Faults in Induction Motors

Detection of failures in induction motors is one of the most important concerns in industry. An unexpected fault in the induction motors can cause a loss of financial resources and waste of time that most companies cannot afford. The contribution of this paper is a fusion of the Empirical Mode Decomposition (EMD) and Multiple Signal Classification (MUSIC) methodologies for detection of multiple combined faults which provides an accurate and effective strategy for the motor condition diagnosis.

A Comprehensive Diagnosis of Hybrid Systems for Discrete and Parametric Faults Using Hybrid I/O Automata

This paper proposes an efficient online monitoring and diagnosis scheme for hybrid systems. The modeling framework is based on extended probabilistic hybrid I/O automata that represent the system behavior, coupling the continuous and discrete dynamics. The diagnosis architecture consists of two primary components: (1) a hybrid observer and (2) a diagnostics module. The hybrid observer employs a focused probabilistic estimation technique to track continuous behavior interspersed with discrete mode changes. The diagnostics module combines fault detection and isolation using a structural residual analysis method. Both discrete and parametric faults are considered in our framework. The working is illustrated on a hybrid two-tank system to demonstrate the effectiveness of this approach.

Real-time SVD-based detection of multiple combined faults in induction motors

Early detection of induction motor faults has been a main subject of investigation for many years. Several approaches have been proposed for identifying one or more faults treated in an isolated way. Multiple combined faults on induction motors represent a big challenge since the reliable diagnosis of a faulty condition under the presence of two or more simultaneous faults is really difficult. This work introduces a novel methodology that merges singular value decomposition, statistical analysis, and artificial neural networks for multiple combined fault identification. Obtained results demonstrate its high effectiveness on detecting faulty bearings, unbalance, broken rotor bars, and all their possible combinations. The developed field programmable gate array-based implementation offers a portable low-cost solution for online classification of the rotating machine condition in real time. Thanks to its generalized nature, the introduced approach can be extended for detecting multiple combined faults under different working conditions by a proper calibration.

Robust fault detection and optimization for a network of unmanned vehicles with imperfect communication channels

The observer-based robust fault detection and optimization for a network of unmanned vehicles with imperfect communication channels and norm bounded modeling uncertainties are addressed. The network of unmanned vehicles is modeled as a discrete-time uncertain Markovian jump system. Based on the model, a residual generator is constructed and the sufficient condition for the existence of the desired fault detection filter is derived in terms of linear matrix inequality. Furthermore, a time domain optimization approach is proposed to improve the performance of the fault detection system. The problem of detecting small faults can be formulated as an optimization problem and its solution is given. For preventing false alarms, a new adaptive threshold function is established. The combined fault detection and optimization algorithm and the adaptive threshold are then applied to a network of highly maneuverable technology vehicles to illustrate the effectiveness of the proposed approach.

Integrated Intelligent Control and Fault System for Wind Generators

The goal of this paper is to show the possibility of combining fault detection analysis, detection, modeling, and control of the doubly-fed induction generator (DFIG) wind turbine using intelligent control and diagnostic techniques. To enable online detection of problems inside the power electronics converter we apply the wave direct analysis method which enables a complete model for fault detection that includes the power electronic stage itself. A neural network system based on Hebbian networks is applied for fault classification with good detection results in simulation. For controlling the wind turbine a number different artificial intelligence techniques are presented including fuzzy logic and an adaptive fuzzy inference systems (ANFIS) which combines the characteristics of fuzzy logic and neural networks. A Grey predictor is also integrated in the control scheme for predicting the wind profile. The combined fault detection and control scheme are validated using simulation results. The software devel...