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The rotary vector (RV) reducer is one of the most important transmission components inside the joints of robots. The high precision motion control of the robot can be realized in the normal operation and service life of agricultural robots. However, the complex structure of the RV reducer cannot fully meet the variable conditions in actual work and the harsh working environment. Mechanical, cracks, and pitting faults can often occur at the same time or successively in actual operation, due to lubrication, manufacturing errors, and unreasonable forces. The signals collected by the sensor are also the mutual coupling of multiple fault sources. Therefore, it is necessary to explore the composite fault diagnosis of the RV reducer. In this study, a blind fault separation was proposed from the compound signals of the RV reducer using time-frequency image ridge extraction and improved sparse component analysis. The fault feature of the RV reducer was then extracted from the core transmission component of agricultural robots. The composite fault diagnosis of the RV reducer was finally realized with the unknown number of fault sources under the reciprocating motion of the joint arm and the time-varying speed of the agricultural robot. Firstly, the ridge extraction from time-frequency images (RETF) was used to synchronously intercept the vibration signals of the manipulator in the constant speed period. Then, the blind source separation (SMF-DPC-OMP) was combined with the sinC function to improve morphological filtering (SMF), density peak clustering (DPC), and orthogonal matching pursuit (OMP), in order to separate and extract the composite faults of stationary signals. The sinC function was taken as a new structural element to construct the average combined morphological filter. The vibration signal in the constant speed period was subjected to morphological filtering for better impact characteristics and sparsity of the signal. The DPC was used to estimate the mixing matrix of sparse signals, and then the sensing matrix was constructed. The separation source signal was reconstructed to combine with the OMP algorithm in the frequency domain. Finally, the reconstructed time-domain signal was subjected to the fast Fourier transform for fault identification. The test bench was utilized to collect the composite fault signals of the sun gear and planetary gear wear of the RV reducer. The results show that the blind separation was effectively realized on the composite faults of the RV reducer. The RETE was used to identify the motion state of the RV reducer, when the timefrequency diagram was blurred, due to the variable speed condition. The SMF-DPC-OMP effectively completed the blind separation of composite faults when the number of fault sources was unknown. The SMF-DPC-OMP saved about 75% of the running time cost than before. A more concise spectrum was achieved to better suppress the fine side frequency and interference components. It is suitable for the composite fault diagnosis of the RV reducer in the articulated industrial robot. The finding can provide a strong reference for the fault feature extraction in the actual production. [ABSTRACT FROM AUTHOR]

When diagnosing the composite fault of the actuator, the characteristics of the motion force of the underwater vehicle are not analyzed, and there are diagnostic errors, resulting in the low accuracy of the diagnosis technology. In order to solve this problem and improve the operation safety of underwater vehicle actuators, this paper proposes a compound fault diagnosis technology for underwater vehicle actuators under positioning error constraints. Analyze the motion force of the underwater robot actuator, control the motion of the underwater robot actuator according to the analysis results, and extract real-time data parameters according to the control results. Under the constraint of positioning error, the composite fault features of the underwater robot actuator are divided, and the diagnosis model is built according to the deep fusion of the features to complete the fault diagnosis. The experimental results show that the technology can diagnose the composite fault data of the actuator, and the positioning error of the horizontal axis and the horizontal axis can be significantly improved, which can improve the diagnosis effect of the composite fault of the actuator and to the improvement of underwater robot running safety of actuators provide certain reference.

Inverters are widely used in the military, industrial production and defense fields as current conversion devices that convert direct current to alternating current. If the inverter fails, it can cause damage to other equipment, resulting in financial losses and, in extreme cases, compromising the safety of users. In this study, by integrating neural networks, the input signals of inverters are quickly converted to Fourier spectrum amplitudes, and from fault signals (such as load phase voltage) to feature vectors. In order to realize automatic extraction and fault detection of inverters, an optimization method is used to determine the appropriate number of nodes in the hidden layer of complex neural networks. The ability to efficiently allocate limited computing, storage, and network resources to meet user demand for services; Continuously optimize quality of service (QoS), including reducing latency, improving bandwidth, and increasing reliability. These problems directly affect the performance and user experience of MEC systems. By studying these issues and proposing corresponding solutions, we aim to improve the performance of MEC systems and provide higher quality services. The accuracy of defect diagnosis can reach higher than 99%, and the method has a high remission rate, demonstrating its effectiveness and benefits.

Abstract In this study, a fault diagnosis model based on magnetic density is proposed to analyse the current characteristics of synchronous generator rotor inter‐turn short circuit and air gap eccentric single fault and composite fault. The model considers the influence of rotor inter‐turn short circuit and air gap eccentricity on air gap magnetic density. According to two main factors, the short circuit turns and eccentric distance, are used to reflect the short circuit and eccentricity of the generator. The detailed parameter input model of the generator can easily and quickly predict the development trend of these current parameters based on the air gap magnetic density. And the phase current is selected as the current parameter. The validity of the proposed model is verified by two‐dimensional finite element analysis and experimental research.

Because of the existence of composite faults, which consist of both short-out and eccentricity faults, the characteristics of the output voltage and internal magnetic field of aviation generators are less different than those of single short-out faults; this causes the eccentricity fault to be difficult to identify. In order to solve this problem, this paper proposes a fault diagnosis method using an enhanced fireworks algorithm (EnFWA) to optimize a deep belief network (DBN). The aviation generator model is built according to the finite element method (FEM), whereas the output of different combinations of composite faults are obtained using simulations. The EnFWA algorithm is used to train and optimize the DBN network to obtain the best structure. Meanwhile, an extreme learning machine (ELM) classifier performs fault diagnosis and classification on the test data. The diagnosis results show that a pinpoint accuracy can be achieved using the proposed method in the diagnosis of composite faults in aviation generators. [ABSTRACT FROM AUTHOR]

In this study, a fault diagnosis model based on magnetic density is proposed to analyse the current characteristics of synchronous generator rotor inter‐turn short circuit and air gap eccentric single fault and composite fault. The model considers the influence of rotor inter‐turn short circuit and air gap eccentricity on air gap magnetic density. According to two main factors, the short circuit turns and eccentric distance, are used to reflect the short circuit and eccentricity of the generator. The detailed parameter input model of the generator can easily and quickly predict the development trend of these current parameters based on the air gap magnetic density. And the phase current is selected as the current parameter. The validity of the proposed model is verified by two‐dimensional finite element analysis and experimental research. [ABSTRACT FROM AUTHOR]

In this paper, a novel composite fault diagnosis method combining adaptive feature mode decomposition (FMD) and minimum noise amplitude deconvolution (MNAD) is proposed. Firstly, chaos mapping and leader mutation selection strategy were introduced to improve the Northern Goshawk algorithm (NGO), and a chaotic leadership Northern Goshawk optimization (CLNGO) algorithm was proposed. The advantages of the CLNGO algorithm in convergence accuracy and speed were verified by 12 benchmark functions. Then, a new index called sparse pulse and cyclicstationarity (SPC) is proposed to evaluate signal sparsity. Finally, SPC is used as the fitness function of CLNGO to optimize FMD and MNAD. The optimal decomposition mode n and filter length of FMD, and filter length L and noise ratio ρ of MNAD are selected. The CLNGO-FMD is used to decompose signal into different modes. The signal is reconstructed based on the kurtosis criterion and the CLNGO-MNAD method is used to reduce the noise of the reconstructed signal twice. The experimental results show that the proposed method can achieve the enhancement of weak features and the removal of noise to extract the fault feature frequency adaptively. Compared with EMD, VMD, MOMEDA, MCKD and other methods, the proposed method has better performance in fault feature frequency extraction, and it is effective for the diagnosis of single faults and composite faults. [ABSTRACT FROM AUTHOR]

To address the difficulty of extracting the features of composite-fault signals under a low signal-to-noise ratio and complex noise conditions, a feature-extraction method based on phase-space reconstruction and maximum correlation Re'nyi entropy deconvolution is proposed. Using the Re'nyi entropy as the performance index, which allows for a favorable trade-off between sporadic noise stability and fault sensitivity, the noise-suppression and decomposition characteristics of singular-value decomposition are fully utilized and integrated into the feature extraction of composite-fault signals by the maximum correlation Re'nyi entropy deconvolution. Verification based on simulation, experimental data, and a bench test proves that the proposed method is superior to the existing methods regarding the extraction of composite-fault signal features. [ABSTRACT FROM AUTHOR]

To address the difficulty of extracting the features of composite-fault signals under a low signal-to-noise ratio and complex noise conditions, a feature-extraction method based on phase-space reconstruction and maximum correlation Re’nyi entropy deconvolution is proposed. Using the Re’nyi entropy as the performance index, which allows for a favorable trade-off between sporadic noise stability and fault sensitivity, the noise-suppression and decomposition characteristics of singular-value decomposition are fully utilized and integrated into the feature extraction of composite-fault signals by the maximum correlation Re’nyi entropy deconvolution. Verification based on simulation, experimental data, and a bench test proves that the proposed method is superior to the existing methods regarding the extraction of composite-fault signal features.

In the noisy environment， the composite fault feature extraction is more difficult. The VMD is widely used in gearbox fault diagnosis， but it is a parametric decomposition method. If K is too large or too small， it will lead to over-decomposition or under-decomposition. The number of layers needs to be determined adaptively， a multi-point kurtosis -VMD （Variational Mode Decomposition） composite fault feature extraction method is proposed. Considering the multi-point kurtosis， the number of impact cycles of multiple faults can be extracted， the number of periodic impacts determines the number K of decomposition layers of the VMD， and after VMD processing， the fault features are further determined by FFT. The proposed adaptive composite fault feature extraction method and Ensemble Empirical Mode Decomposition （EEMD）comparison analysis verify that it can overcome the characteristics of modal aliasing. The effectiveness of this method is further determined by the measured signal processing. The composite fault characteristics such as gear spalling and bearing balls are finally determined.

In order to identify the nonlinear nonstationary pitting-wear fault signal of gears in gearbox, a new method of composite fault diagnosis for gearbox is proposed, which combines empirical mode decomposition with improved variational mode decomposition. Aiming at the problem of false modes in the results of empirical mode decomposition when processing signals, the energy method is used to eliminate and update the mode components, and the correlation coefficient method is used to calculate the correlation between the updated mode components and the original signal. The components with strong correlation are selected to form the combined mode components to weaken the noise and improve the signal-to-noise ratio. Aiming at the problem that variational mode decomposition method needs to manually determine the number of mode components k and the penalty factor α during signal decomposition, a combination of envelope spectral entropy and waveform method is proposed to determine the optimal parameter combination. By analyzing the pitting-wear composite fault vibration signals of gears in the gearbox and the normal signals of the gearbox, the effectiveness of the proposed method is verified, and a comparative analysis with the empirical mode decomposition method is performed to highlight the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Due to the improper selection of the method,the leakage diagnosis or misdiagnosis usually occurs when the composite fault features in the gearbox are extracted. As a result of noise,there will be the phenomena of mode aliasing after LMD decomposition. In view of this,a hybrid fault diagnosis method of the gearbox based on CPFs-MOMEDA is proposed. First,the original signal is denoised by LMD,and a series of PFs are obtained. And the false component and the residual component are removed by the correlation coefficient method. Then,the definition of multi-point kurtosis is introduced,and the fault feature period is extracted by calculating the multi-point kurtosis spectrum of each layer of PF,and the PF without periodic impact is eliminated. In order to maintain the integrity of the original signal,PFs with the same period are recombined by the combined product function method. Finally,the recombinant signal is denoised by MOMEDA by setting different period intervals to further extract the fault characteristics. And the method is applied to the feature extraction of gearbox composite faults to verify the feasibility.

Copyright of Journal of Dalian University of Technology / Dalian Ligong Daxue Xuebao is the property of Journal of Dalian University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Compound fault diagnosis is challenging due to the complexity, diversity and non-stationary characteristics of mechanical complex faults. In this paper, a novel compound fault separation method based on singular negentropy difference spectrum (SNDS) and integrated fast spectral correlation (IFSC) is proposed. Firstly, the original signal was de-noised by SNDS which improved the noise reduction effect of singular difference spectrum by introducing negative entropy. Secondly, the de-noised signal was analyzed by fast spectral correlation. Finally, IFSC took the fourth-order energy as the index to determine the resonance band and separate the fault features of different single fault. The proposed method is applied to analyze the simulated compound signals and the experimental vibration signals, the results show that the proposed method has excellent performance in the separation of rolling bearing composite faults.

When diagnosing the composite fault of the actuator, the characteristics of the motion force of the underwater vehicle are not analyzed, and there are diagnostic errors, resulting in the low accuracy of the diagnosis technology. In order to solve this problem and improve the operation safety of underwater vehicle actuators, this paper proposes a compound fault diagnosis technology for underwater vehicle actuators under positioning error constraints. Analyze the motion force of the underwater robot actuator, control the motion of the underwater robot actuator according to the analysis results, and extract real-time data parameters according to the control results. Under the constraint of positioning error, the composite fault features of the underwater robot actuator are divided, and the diagnosis model is built according to the deep fusion of the features to complete the fault diagnosis. The experimental results show that the technology can diagnose the composite fault data of the actuator, and the positioning error of the horizontal axis and the horizontal axis can be significantly improved, which can improve the diagnosis effect of the composite fault of the actuator and to the improvement of underwater robot running safety of actuators provide certain reference., Competing Interests: The authors declare that there is no conflict of interest regarding the publication of this paper., (© 2023 The Authors.)

Aiming at the problem that the composite fault signal of the gearbox is weak and the fault characteristics are difficult to extract under strong noise environment, an improved singular spectrum decomposition (ISSD) method is proposed to extract the composite fault characteristics of the gearbox. Singular spectrum decomposition (SSD) has been proved to have higher decomposition accuracy and can better suppress modal mixing and pseudo component. However, noise has a great influence on it, and it is difficult to extract weak impact components. In order to improve the limitations of SSD, we chose the minimum entropy deconvolution adjustment (MEDA) as the pre-filter of the SSD to preprocess the signal. The main function of the minimum entropy deconvolution adjustment is to reduce noise and enhance the impact component, which can make up for the limitations of SSD. However, the ability of MEDA to reduce noise and enhance the impact signal is greatly affected by its parameter, the filter length. Therefore, to improve the shortcomings of MEDA, a parameter adaptive method based on Cuckoo Search (CS) is proposed. First, construct the objective function as the adaptive function of CS to optimize the MEDA algorithm. Then, the pre-processed signal is decomposed into singular spectral components (SSC) by SSD, and the meaningful components are selected by Correlation coefficient. For the existing modal mixing phenomenon, the SSC component is reconstructed to eliminate the misjudgment of the result. Then, the frequency spectrum analysis is performed to obtain the frequency information for fault diagnosis. Finally, the effectiveness and superiority of ISSD are validated by simulation signals and applying to compound faults of a Gear box test rig.

This paper investigates the stator circulating current characteristics under the static air-gap eccentricity & stator inter-turn short circuit composite faults, and the effect of the faulty parameter development on the circulating current. The air-gap magnetic flux density is firstly deduced, based on which the electromotive force (E.M.F.) difference between the two parallel-branches is obtained. Then the harmonic characteristics of the circulating current, also the relationship between the faulty parameters and the circulating current are investigated. Finally, the experiments are taken on a SDF-9 fault simulating generator to verify the theoretical analysis. The result shows that there will be circulating current of 1st and 3rd harmonics induced inside the parallel-branch loop under the composite faults. Moreover, the development of the stator inter-turn short circuit, the increment of the static air-gap eccentricity, and the rise of the exciting current will all make the 1st and 3rd harmonic components of the circulating current increased. The investigation presented in this paper will be beneficial to the failure criterion and the diagnosis of the static air-gap eccentricity & stator short circuit composite fault of turbo-generator. [ABSTRACT FROM AUTHOR]

This article investigates the characteristics of the stator winding parallel branches circulating the current of turbogenerators under air gap eccentricity and rotor inter-turn short-circuit composite faults. The presented current characteristics are achieved from the theoretical derivation of the electromotive force difference between two parallel branches and are verified by the experimental results. Three types of the composite faults are studied. The theoretical analysis and the experimental results show the significant difference of the circulating current characteristics between a normal condition and a composite fault condition. An effective diagnosis method for the composite faults based on the circulating current increment is proposed in the end. The investigation results of this article will be beneficial to the composite fault diagnosis of turbogenerators. [ABSTRACT FROM AUTHOR]

Compound fault diagnosis is challenging due to the complexity, diversity and non-stationary characteristics of mechanical complex faults. In this paper, a novel compound fault separation method based on singular negentropy difference spectrum (SNDS) and integrated fast spectral correlation (IFSC) is proposed. Firstly, the original signal was de-noised by SNDS which improved the noise reduction effect of singular difference spectrum by introducing negative entropy. Secondly, the de-noised signal was analyzed by fast spectral correlation. Finally, IFSC took the fourth-order energy as the index to determine the resonance band and separate the fault features of different single fault. The proposed method is applied to analyze the simulated compound signals and the experimental vibration signals, the results show that the proposed method has excellent performance in the separation of rolling bearing composite faults. [ABSTRACT FROM AUTHOR]

Due to the existence of multiple rotating parts in the planetary gearbox—such as the sun gear, planet gears, planet carriers, and its unique planetary motion, etc.—the vibration signals generated under multiple fault conditions are time-varying and nonstable, thus making fault diagnosis difficult. In order to solve the problem of planetary gearbox composite fault diagnosis, an improved particle swarm optimization variational mode decomposition (IPVMD) and improved convolutional neural network (I-CNN) are proposed. The method takes as input the spectrum of the original vibration signal that contains rich information. First, the automatic feature extraction of signal spectrum is performed by I-CNN, while a classifier is used to diagnose the fault modes. Second, the composite fault signal is decomposed into multiple single fault signals by adaptive variational mode, and the signal is decomposed as a model input to diagnose the single fault component. Finally, a complete intelligent diagnosis of planetary gearboxes is conducted. Through experimental verification, the composite fault diagnosis method combining IPVMD and I-CNN will diagnose the composite fault and effectively diagnose the sub-fault included in the composite fault. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the composite fault signal of the gearbox is weak and the fault characteristics are difficult to extract under strong noise environment, an improved singular spectrum decomposition (ISSD) method is proposed to extract the composite fault characteristics of the gearbox. Singular spectrum decomposition (SSD) has been proved to have higher decomposition accuracy and can better suppress modal mixing and pseudo component. However, noise has a great influence on it, and it is difficult to extract weak impact components. In order to improve the limitations of SSD, we chose the minimum entropy deconvolution adjustment (MEDA) as the pre-filter of the SSD to preprocess the signal. The main function of the minimum entropy deconvolution adjustment is to reduce noise and enhance the impact component, which can make up for the limitations of SSD. However, the ability of MEDA to reduce noise and enhance the impact signal is greatly affected by its parameter, the filter length. Therefore, to improve the shortcomings of MEDA, a parameter adaptive method based on Cuckoo Search (CS) is proposed. First, construct the objective function as the adaptive function of CS to optimize the MEDA algorithm. Then, the pre-processed signal is decomposed into singular spectral components (SSC) by SSD, and the meaningful components are selected by Correlation coefficient. For the existing modal mixing phenomenon, the SSC component is reconstructed to eliminate the misjudgment of the result. Then, the frequency spectrum analysis is performed to obtain the frequency information for fault diagnosis. Finally, the effectiveness and superiority of ISSD are validated by simulation signals and applying to compound faults of a Gear box test rig. [ABSTRACT FROM AUTHOR]