Local Fault Diagnosis Research Articles

Research on Local Fault Diagnosis of Rotary Vector Reducer Based on Motor Current Signature Analysis

The rotary vector (RV) reducer, as a highly precise transmission mechanism, can lead to a series of consequential hazards when experiencing local faults. Conducting corresponding research on performance monitoring and fault diagnosis holds significant importance. In order to achieve higher diagnostic accuracy, better classification, prediction effect, and less use of sensors, this paper proposes a local fault diagnosis method of RV reducer based on Motor current signature analysis (MCSA). Firstly, the centre gear local fault electromechanical coupling model is created by combining the working principle of the servo motor, the working characteristics of the RV reducer, and the influencing factors of motor current. Then, according to the actual operating conditions of industrial robots (IRs), the corresponding experimental platform is designed and constructed, and current signals in four different modes are collected. Fast Fourier transform (FFT) is performed on the current signals to obtain frequency domain features, and the correctness of the local fault coupling model of the centre gear is experimentally verified. Finally, the time‐domain statistical features, time–frequency domain features, and CNN features of servo‐feedback current signals under different rotational speeds are extracted and used in the implementation of local fault diagnosis for the RV reducer, respectively. In this paper, it is confirmed that MCSA can be used for localized fault diagnosis of the RV reducer, and combined with a deep learning network, it can effectively predict the fault modes with an average accuracy of more than 96%.

Operando analysis of in-plane heterogeneity for the PEM electrolyzer cell: Mappings of temperature and current density

The internal multi-physical fields of a proton exchange membrane electrolyzer cell (PEMEC) are affected by various factors, exhibiting an intricate coupling state. During electrolysis process, the complexity manifests as heterogeneous distribution of in-plane temperature and current density, potentially leading to local hot spots and performance degradation. Such challenges pose threats to the lifespan and operational safety of PEMEC. This study developed temperature measurement endplates and segmented printed circuit board (PCB), enabling operando measurement of in-plane temperature and current density distribution inside a 25 cm2 lab-scale PEMEC. The mappings of temperature and current density were plotted, and the in-plane heterogeneity was assessed by the introduced indexes. The results indicate that variations in operational parameters affect the balance among cell heat production, heat dissipation, and heat input in the flow area, leading to temperature distribution heterogeneity. The migration mechanism of high-temperature zone is elucidated. Factors such as water-gas flow and local conduction contribute to the heterogeneity in current density distribution. Additionally, specific operating conditions including cold and warm start-up, and water starvation are taken as cases, and a detailed analysis is conducted on the dynamic behaviors of current and temperature distribution and the heterogeneity evolution. This work not only provides experimental evidence for the electrical-thermal characteristics inside the PEMEC but also offers insights for local performance evaluation and operation fault diagnosis in practical applications.

Analysis of Vibration Characteristics of Planetary Gearbox with Broken Sun Gear Based on Phenomenological Model

To investigate the vibration properties in healthy and fault conditions of planetary gearboxes, a phenomenological model is constructed to present the vibration spectrum structure. First, the effects of the base deflection of the gear fillet and the flexibility between the root circle and the base circle on the time-varying meshing stiffness are considered in order to construct an equivalent model of time-varying mesh stiffness and broken tooth faults, exploring the law of variation for meshing stiffness when differently sized faults occur on the sun gear. Then, considering both the effect of the vibration transfer path and the meshing impacts, we establish phenomenological models of planetary gears under healthy and fault conditions. By comparing and analyzing the phenomenological model based on the cosine function to verify the effectiveness of the proposed model. The experimental results show that the error of the proposed model is 1.38% lower than that of the traditional phenomenological model, and the proposed model can accurately analyze the frequency, amplitude, and sideband characteristics of the vibration signals of sun gear with different degrees of broken tooth, which can be used for the local fault diagnosis of planetary gearboxes.

Weak local fault diagnosis of gearboxes based on adaptive inertia factor particle swarm independent component analysis

Due to the interference of surrounding noise when collecting the vibration signal of a fixed shaft gearbox, it is impossible to extract the fault features contained in the vibration signal with a high degree of accuracy and this reduces the accuracy of fault diagnosis of the gearbox. Aiming at this problem, this paper proposes a method for local weak fault diagnosis of gears based on improved independent component analysis (ICA). Firstly, for the shortcomings of ICA, such as high requirements for initial value selection, ease of falling into local extrema and the need to derive formulae in advance, this paper proposes to improve the separation performance of the algorithm by combining ICA with particle swarm optimisation (PSO). Also aiming at the shortcomings of slow convergence of PSO and decreased searchability in the later iteration, this paper proposes an adaptive inertia weight particle swarm optimisation (AIWPSO) algorithm by introducing the roulette idea into PSO. Then, combining ICA with AIWPSO, an independent component analysis method for adaptive inertia weight particle swarm optimisation (AIWPSO-ICA) is proposed to improve the signal separation performance. Finally, based on AIWPSO-ICA, a method for diagnosing weak local faults of gears is offered. The simulation signals and the real data experimental results verify the effectiveness and superiority over conventional AIWPSO-ICA.

A Deep-Learning-Based Fault Diagnosis Method of Industrial Bearings Using Multi-Source Information

In recent years, the industrial motor bearing fault diagnosis method based on deep learning and multi-source information fusion has made some research progress, and research results show that the uncertainty of noise interference and signal measurement error has been improved to a certain extent. However, the multi-source heterogeneous information of industrial motor bearings not only improves the stability and fault tolerance of the bearing fault diagnosis model but also brings conflicts in information fusion. If the conflicts caused by multi-source information cannot be reasonably resolved, it will be difficult to make further judgments on the bearing faults of industrial motors. Therefore, solving the multi-source information conflict effectively while fully using the complementarity of bearing multi-source heterogeneous information is an urgent problem to be solved in developing industrial motor-bearing fault diagnosis technology. This paper proposes an industrial motor bearing fault diagnosis algorithm based on multi-local model decision conflict resolution (MLMF-CR) to fully integrate multi-source heterogeneous information and reasonably resolve multi-source information conflicts. After the initial characteristic signal selection and cleaning of the vibration and current signals of industrial motor bearings, the algorithm deeply excavates the characteristic information of the bearing signals in each fault state through the local fault diagnosis model based on the bidirectional long short-term memory network (Bi-LSTM) and forms a local diagnosis. After the decision is made, evidence theory is used for fusion. In addition, the high conflict situation that may occur in the process of decision-making fusion is also considered. To this end, the trust degree distribution is introduced to reduce information conflict. Specifically, according to the difference in the sensitivity and reliability of bearing faults under different operating environments or specific conditions, the degree of difference in faults is refined into balanced sensitivity and unbalanced sensitivity. When the fault sensitivity is balanced, the trust of different information sources is quantified by support and uncertainty. When the sensitivity is unbalanced, gray relational analysis is used to assign trust degrees to different information sources. The algorithm can effectively resolve the high degree of conflict in the decision-making fusion process while considering the complementarity of multi-source heterogeneous information. Experiments evaluate the effectiveness of the proposed method.

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.

Novel Fault Diagnosis of Bearings and Gearboxes Based on Simultaneous Processing of Spectral Kurtoses

Diagnosis of bearings and gears, traditionally uses the envelope (i.e., demodulation) approach. The spectral kurtosis (SK) is a technique used to identify frequency bands for demodulation. These frequency bands are related to the structural resonances, excited by a series of fault-induced impulses. The novel approach for bearing/gear local fault diagnosis is proposed, based on division of bearing/gear vibration signals into specially defined short duration segments and simultaneous processing of SKs of all these segments for damage diagnosis. The SK-filtered vibrations are used for diagnostic feature extraction further subjected to the decision-making process, based on k-means and k-nearest neighbors. The important feature of the proposed approach is robustness to random slippage in bearings. The experimental validation of a bearing inner race local defects (1.2% relative damage size), and simulated gear vibration (15% relative pitting size), shows a very good diagnostic performance on bearing vibrations and gear vibrations to diagnose local faults. Novel diagnostic effectiveness comparison between the proposed technology and wavelet-based technology is performed for diagnosis of local bearing damage.

A Novel Impact Feature Extraction Method Based on EMD and Sparse Decomposition for Gear Local Fault Diagnosis

Sparse decomposition has been widely used in gear local fault diagnosis due to its outstanding performance in feature extraction. The extraction results depend heavily on the similarity between dictionary atoms and fault feature signal. However, the transient impact signal aroused by gear local defect is usually submerged in meshing harmonics and noise. It is still a challenging task to construct high-quality impact dictionary for complex actual signal. To handle this issue, a novel impact feature extraction method based on Empirical Mode Decomposition (EMD) and sparse decomposition is proposed in this paper. Firstly, EMD is employed to adaptively decompose the original signal into several Intrinsic Mode Functions (IMFs). The high-frequency resonance component is separated from meshing harmonics and part of the noise. Then, the IMF with the prominent impact features is selected as the Main Intrinsic Mode Function (MIMF) based on the kurtosis. Accordingly, the modal parameters required for impact dictionary are identified from the MIMF by correlation filtering. Finally, the transient impact component is extracted from the original signal by Match Pursuit (MP). The proposed method was adequately evaluated by a gear local fault simulation signal, and the single-stage gearbox and five-speed transmission experiments. The effectiveness and superiority of the proposed method is validated by comparison with other feature extraction techniques.

Phasor measurement unit based local fault detection in distribution systems

Distribution systems with advanced metering infrastructure offer many opportunities for fault detection. A novel approach is presented in this paper for local fault diagnosis in neighbour area networks by utilizing data concentrators. A lightweight local diagnostic function is presented based on first engineering principles. It is shown that from four phasor measurement unit data, a fault can be detected between buses and fault parameters can be evaluated. The diagnosis of a neighbour area network is accomplished as a sequential application of the local method. Edge cases and limitations of the method are explored. The applicability of the method is demonstrated on a real world case study. By introducing threshold values for the fault parameters, the results fulfill the engineering expectations.

Phenomenological vibration models of planetary gearboxes for gear local fault diagnosis

Planetary gearboxes (PGs) are prone to gear local faults, such as cracking, pitting, and spalling, because of their difficult working environment. These faults may lead to significant capital losses and catastrophic events. Numerous phenomenological modulation models of vibration signals of PGs have been proposed for diagnosis purposes. However, local fault diagnosis remains extremely challenging due to two aspects: (1) Most vibration modulation models cannot reflect the local fault-induced pulse modulation characteristic. (2) Studies on local fault characteristic frequencies (LFCFs) of gears lack a unified mechanism explanation. To solve these problems, an improved phenomenological vibration model of a gear pair with local fault is first proposed. This model can reflect the pulse modulation characteristic. Then, relative phases between multiple gear pairs in the PG are calculated based on a modified phase model. Based on these, phenomenological vibration models of PGs with/without local faults are established, spectral structures are derived using Fourier series analysis, and LFCFs are deduced for local fault diagnosis. Simulation and experimental studies demonstrate the effectiveness of the proposed models, which exhibit higher descriptive capability than traditional models.

An Intelligent Diagnosis Method for Machine Fault Based on Federated Learning

In engineering, the fault data unevenly distribute and difficultly share, which causes that the existing fault diagnosis methods cannot recognize the newly added fault types. An intelligent diagnosis method for machine fault is proposed based on federated learning. Firstly, the local fault diagnosis models diagnosing the existing fault data and the newly added fault data are established by deep convolutional neural network. Then, the weight parameters of local models are fused into global model parameters by federated learning. Finally, the global model parameters are transmitted to each local model. Therefore, each local model update into a global shared model which can recognize the newly added fault types. The proposed method is verified by bearing data. Compared with the traditional model, which can only diagnose the existing fault data but cannot recognize newly added fault types, the federated fault diagnosis model fusing weight parameters can diagnose newly added faults without exchanging the data, and the accuracy is 100%. The proposed method provides an effective method to solve the poor sharing of fault data and poor generalization of fault diagnosis model for mechanical equipment.

Distributed Diagnosis of Sensor and Actuator Faults in Air Handling Units in Multi-Zone Buildings: A Model-Based Approach

The presence of sensing and actuating equipment faults in Heating, Ventilation and Air-Conditioning (HVAC) systems may result in unsatisfactory indoor thermal conditions and significant wasted energy in the building sector. The design of fault diagnosis algorithms for multi-zone buildings is a highly challenging procedure due to the overall complexity of the system. This paper proposes a distributed model-based fault diagnosis algorithm for detecting, isolating and identifying faults that can affect the actuating devices of Air Handling Units (AHUs) and the sensor devices. Based on modeling the temperature dynamics of each local AHU and its underlying zone with respect to its neighboring zones, a local fault diagnosis agent that collects local control signals and local and neighboring sensor measurements is designed. Each local diagnosis agent is designed to estimate on-line the states of each AHU. For each estimation, a residual signal is derived that corresponds to the difference between the measured and estimated value at each time step. Considering healthy conditions, the residual is bounded by its corresponding adaptive threshold that is calculated considering the effects of modeling uncertainties. Each corresponding pair of residual and adaptive threshold forms an analytical redundancy relation which indicates the possible presence of one or more faults. Exploiting the dependency of each actuator and sensor fault with each Analytical Redundancy Relation (ARR), a decision logic is designed, that can reveal under certain conditions the location and type of the fault during the operation of the system. The proposed algorithm is evaluated using a simulation example of a primary school building with 25 zones.

Weak Feature Extraction of Local Gear Damage Based on Underdamped Asymmetric Periodic Potential Stochastic Resonance

The enhancement of the detection of weak signals against a strong noise background is a key problem in local gear fault diagnosis. Because the periodic impact signal generated by local gear damage is often modulated by high-frequency components, fault information is submerged in its envelope signal when demodulating the fault signal. However, the traditional bistable stochastic resonance (BSR) system cannot accurately match the asymmetric characteristics of the envelope signal because of its symmetrical potential well, which weakens the detection performance for weak faults. In order to overcome this problem, a novel method based on underdamped asymmetric periodic potential stochastic resonance (UAPPSR) is proposed to enhance the weak feature extraction of the local gear damage. The main advantage of this method is that it can better match the characteristics of the envelope signal by using the asymmetry of its potential well in the UAPPSR system and it can effectively enhance the extraction effect of periodic impact signals. Furthermore, the proposed method enjoys a good anti-noise capability and robustness and can strengthen weak fault characteristics under different noise levels. Thirdly, by reasonably adjusting the system parameters of the UAPPSR, the effective detection of input signals with different frequencies can be realized. Numerical simulations and experimental tests are performed on a gear with a local root crack, and the vibration signals are analyzed to validate the effectiveness of the proposed method. The comparison results show that the proposed method possesses a better resonance output effect and is more suitable for weak fault feature extraction under a strong noise background.

Research on a feature extraction method for local faults in planetary gearboxes based on improved dynamic time warping

Aiming at the local fault diagnosis of planetary gearbox gears, a feature extraction method based on improved dynamic time warping (IDTW) is proposed. As a calibration matching algorithm, the dynamic time warping method can detect the differences between a set of time-domain signals. This paper applies the method to fault diagnosis. The method is simpler and more intuitive than feature extraction methods in the frequency domain and the time-frequency domain, avoiding their limitations and disadvantages. Due to the shortcomings of complex calculation, singularity and poor robustness, the paper proposes an improved method. Finally, the method is verified by envelope spectral feature analysis and the local fault diagnosis of gears is realised.

Performance analysis of edge-PLCs enabled industrial Internet of things

With the recent advancement in Industrial Internet of Things (IIoT), general programmable logic controllers (PLCs) have been playing more and more critical roles in industrial control systems (ICSs), such as providing local data processing, decentralized control and fault diagnosis. These so called edge-PLCs, directly receive the raw data from sensors embedded in factory equipments, put them into predefined memory space and perform analysis using programs such as the ladder logic. The challenge is how to allocate blocks in the fixed-size memory to different sensors so as to match irregular data flows. In this paper, we try to conduct performance analysis of different partition instances of the memory in the edge-PLC by modeling this problem as a multiple single-server queueing systems. We assume every sensing flow is independent of each other and has its dedicated processer. Changes can be made to partition instances to adapt to the external environment, such as the rising of order numbers or product category switching. Each state of the environment is defined by the finite state Markov chain and arrival of sensing data flows follow the stationary Poisson process. The data in the queue will expire after staying in the memory for a while. The duration of availability and service is modeled as the exponential distribution. The performance measured under different system states are analyzed in the simulation.

Cyclostationary modeling for local fault diagnosis of planetary gear vibration signals

Owing to the rotation and reciprocation of mechanical equipment, their vibration signals inherently exhibit cyclic stationary characteristics. This paper provides a phenomenological signal model of the planetary gear vibrations under the cyclostationarity paradigm. The model is helpful to give comprehensive demodulation information which is the limit of classic spectral analysis of deterministic signals, so that the diagnosis of local faults in planetary gear sets can be more explicit. First, the effects of rotating speed fluctuation on periodic gear meshing vibrations are analyzed. It is shown that the stationary speed fluctuation will turn the periodicity of signals into second-order cyclostationarity. Next, an impulsive oscillation sequence with interval timing jitter is used to describe the dynamic response of the meshing stiffness change caused by local defects. Meanwhile, the influence of a vibration transfer path is also considered in the model. Based on the above analysis, the following procedure is utilized to analyze planetary gearbox vibrations: remove the periodic components by the self-adaptive noise cancellation method and then, apply the second-order frequency descriptor to reveal fault features of the signals. Both the simulation and experiment are used to validate the correctness of model derivation and the superiority of the cyclostationary analysis.

Distributed adaptive fault‐tolerant leader‐following formation control of nonlinear uncertain second‐order multi‐agent systems

SummaryThis paper presents a distributed integrated fault diagnosis and accommodation scheme for leader‐following formation control of a class of nonlinear uncertain second‐order multi‐agent systems. The fault model under consideration includes both process and actuator faults, which may evolve abruptly or incipiently. The time‐varying leader communicates with a small subset of follower agents, and each follower agent communicates to its directly connected neighbors through a bidirectional network with possibly asymmetric weights. A local fault diagnosis and accommodation component are designed for each agent in the distributed system, which consists of a fault detection and isolation module and a reconfigurable controller module comprised of a baseline controller and two adaptive fault‐tolerant controllers, activated after fault detection and after fault isolation, respectively. By using appropriately the designed Lyapunov functions, the closed‐loop stability and asymptotic convergence properties of the leader‐follower formation are rigorously established under different modes of the fault‐tolerant control system.

A Method for Local Parametric Fault Diagnosis of a Broad Class of Analog Integrated Circuits

This paper is devoted to local parametric fault diagnosis of nonlinear analog integrated circuits designed in a bipolar and CMOS technology. An algorithm is proposed that allows estimating values of the considered set of the parameters. The algorithm exploits a diagnostic test performed in a dc state, leading to output voltages measured in the circuit. Each of the output voltages is a parameterized function. A system of overdetermined equations fitting the parameterized functions to the data points is created. An efficient iterative method is developed for solving this overdetermined system of nonlinear equations exploiting the idea of the normal equation and a homotopy concept. It is implemented in the manner that allows operating with the functions not given in the explicit analytical form, which commonly occurs in real nonlinear circuits. For illustration, two examples are given, including a circuit with bipolar transistors and a CMOS circuit designed in a nanometer technology. They reveal the effectiveness of the proposed method.

Multiple soft fault diagnosis of DC analog CMOS circuits designed in nanometer technology

This paper is devoted to local multiple soft fault diagnosis of nonlinear DC analog CMOS circuits designed in nanometer technology. An algorithm is developed that allows estimating the values of a set of potentially faulty process parameters. It exploits two tests with the input nodes accessible for excitation and the output node accessible for measurement. One of the tests is used to find the parameter values. It leads to a system of nonlinear algebraic type equations that are not given in explicit analytical form and may be satisfied by several sets of the parameter values. To solve the system of the equations the Nelder---Mead optimization method is applied with the objective function properly modified during the computation process. Next the obtained solution, being a set of the parameter values, is validated using the other test. If the solution passes this test it is considered as the actual one. Otherwise, another solution is calculated and verified using the same approach. The developed diagnostic procedure has been implemented in DELPHI, whereas the required by the algorithm circuit analyses are performed using IsSPICE 4 and both environments have been joined together. For illustration three numerical examples are given.

An automatic abrupt information extraction method based on singular value decomposition and higher-order statistics

One key aspect of local fault diagnosis is how to effectively extract abrupt features from the vibration signals. This paper proposes a method to automatically extract abrupt information based on singular value decomposition and higher-order statistics. In order to observe the distribution law of singular values, a numerical analysis to simulate the noise, periodic signal, abrupt signal and singular value distribution is conducted. Based on higher-order statistics and spectrum analysis, a method to automatically choose the upper and lower borders of the singular value interval reflecting the abrupt information is built. And the selected singular values derived from this method are used to reconstruct abrupt signals. It is proven that the method is able to obtain accurate results by processing the rub-impact fault signal measured from the experiments. The analytical and experimental results indicate that the proposed method is feasible for automatically extracting abrupt information caused by faults like the rotor–stator rub-impact.