Rotating machine fault diagnosis based on denoising source separation

In this paper, the direction of arrival (DOA) and adaptive antenna array beamforming for the smart antenna applications are realized by using Minimum Variance Distortionless Response (MVDR) algorithm of DOA estimation. The MVDR supported by joint approximate diagonalization of eigenmatrices (JADE) algorithm is used to estimate the direction of arrivals of coherent signals in non-coherent groups. The generalized steering vectors using JADE algorithm are first estimated, and then the spectrum methods are realized to estimate the DOA of each signal. Root mean square error (RMSE) values are computed in different scenarios for the proposed method as well as JADE-MUSIC and the corresponding results are compared. Although both methods resolved the signals correctly, simulation results show that JADE based MVDR has better performances in the cases of low number of antenna elements and snapshots. The DOAs obtained are then processed using LMS adaptive beamforming algorithm to steer the main lobe of the radiation pattern to desired angles (signals) and the nulls to undesired angles (signals). The simulation results also reveal that adaptive beamforming is successfully done by annihilating the effects of undesired signals significantly.

Research on deep learning has been increasing in recent years in the field of fault diagnosis in rotary machine. However, compared to training data, real world data is collected from different system conditions and environments. Therefore, real world data has different datadistribution and various noise with the training data, leading to domain shift between data. Due to the problem mentioned above, deep learning often fails to apply on industrial data.Domain generalization is an emerging deep learning technique to generalize domain discrepancy. In this study, domain adversarial neural network (DANN)-based domaingeneralization is proposed for multi-label fault diagnosis of rotary machine. Frequency domain image data were generated via implementing short time fourier transform(STFT) to the sensor data collected from the test rig. Then, the features are utilized as training data to diagnosis multi-label fault via DANN-based domain generalization. Moreover, the upper boundary of rotating speed domain of the rotary machine where domain generalization can effectively diagnosis multi-label fault is suggested.

An angle error extraction algorithm based on JADE for three-channel radar seeker system with the existence of deception jamming

Machine learning applications in power system fault diagnosis: Research advancements and perspectives

The environmentally clean nature of solar photovoltaic (PV) technology causes PV power generation to be embraced by all countries across the globe. Consequently, installation and utilization of PV power systems have seen much growth in recent years. Although PV arrays of such systems are robust, they are not immune to faults. To guarantee reliable power supply, economic returns, and safety of both humans and equipment, highly accurate fault detection, diagnosis, and interruption devices are required. In this paper, an overview of four major PV array faults and their causes are presented. Specifically, ground fault, line-line fault, arc fault, and hot spot fault have been covered. Next, conventional and advanced fault detection and diagnosis (FDD) techniques for managing these faults are reviewed. Moreover, a single evaluation metric has been proposed and utilized to evaluate the performances of the advanced FDD techniques. Finally, based on the papers reviewed, PV array fault management future trends and possible recommendations have been outlined.

The main challenge with rotating machine fault diagnosis is the condition monitoring of machines undergoing nonstationary operations. One possible way of efficiently handling this situation is to use the deep learning (DL) method. However, most DL methods have difficulties when the issue of imbalanced datasets occurs. This paper proposes a novel framework to mitigate this issue by developing an area-metric-based sampling method. In the proposed process, the new sampling scheme can identify which locations of the datasets can potentially have a high degree of surprise. The basic idea of the proposed method is whenever significant deviations from the area metrics are observed to populate more sample points. In addition, to improve the training accuracy of the DL method, the obtained sampled datasets are transformed into a continuous wavelet transform (CWT)-based scalogram representing the time–frequency component. The dilated convolutional neural network (CNN) is also introduced as a classification process with the altered images. The efficacy of the proposed method is demonstrated with fault diagnosis problems for welding robots. The obtained results are also compared with existing methods.

Electroencephalogram (EEG) extraction has widely used Stone's Blind Source Separation (Stone's BSS) algorithm. However, Stone's BSS algorithm is sensitive to the initial half-life (hlong, hshort) and weight vector W parameters, which affect the convergence of the algorithm. This paper proposes a hybridization of Stone's BSS with Particle Swarm Optimization (PSO) to boost the separation process. An improved Stone's BSS (ISBSS) method is employed to reject eye blinking from the electroencephalogram (EEG) mixture. The electroencephalogram (EEG) mixed-signal is first centralized and whitened; then, it is incorporated into the particle swarm optimization (PSO) iterative algorithm to process the initial (hlong, hshort) and generate the weight vector W parameters randomly. Finally, the generalized eigenvalue decomposition (GEVD) method is applied to extract EEG singles to obtain a clean EEG signal. A clinical EEG database is used to test the improved and other algorithms. The GEVD method estimates the measurement performance of the proposed algorithm using a carrier-to-interference ratio and integral square error and compares the proposed algorithm with the conventional Stone's BSS, fast independent component analysis (FastICA), evolutionary fast independent component analysis (EFICA), and joint approximate diagonalization of eigen matrices (JADE) algorithms to check its effectiveness. The results show that the suggested hybrid method has a better performance and decreasing elapsed time than conventional Stone's BSS and other algorithms.

With the deepening advancement of smart manufacturing, regular fault diagnosis plays a crucial role in optimizing productivity and maintaining equipment safety and stability. Joint domain adaptation (JDA) techniques for fault diagnosis have garnered significant attention; however, their ability to characterize nonlinear, high-dimensional features remains limited. Additionally, low pseudo-label confidence levels result in insufficient distribution alignment, leading to domain drift that compromises cross-domain fault diagnosis performance. Based on this, this paper proposes a multi-kernel higher-order moment matching joint domain adaptive network (MKHoJDA) for cross-domain mechanical fault diagnosis. Firstly, a multi-scale dilatable residual convolution (MDRC) module is proposed to extract transferable features from both the source and target domains more accurately. Secondly, to address the limitation of traditional JDA in statistically measuring higher-order features, a multi-kernel higher-order moment matching (MKHoMM) statistical metric strategy is designed. Multiple kernel functions are combined to create a more expressive composite kernel, enhancing the power of feature mappings, reducing distribution discrepancies between domains. Additionally, to address the dependency of conditional distribution alignment (CDA) on pseudo-label accuracy, a prototype-aware pseudo-label feedback (PPF) mechanism was developed. This mechanism evaluates sample confidence using Jensen-Shannon (JS) divergence, selects high-confidence target domain samples for pseudo-label generation, and dynamically updates prototype centers. This approach effectively mitigates the negative impact of pseudo-labels while improving CDA performance. Finally, multiple cross-domain fault diagnosis experiments were conducted on the JNU and PU datasets. The average accuracy significantly surpassed that of other advanced comparative models, demonstrating that the proposed algorithm effectively mitigates the impact of data distribution differences and exhibits exceptional cross-domain fault diagnosis performance.

Intelligent bearing fault diagnosis with domain adaptations has accomplished remarkable performances under various operating conditions. However, especially for different load conditions, the model bias due to the physical characteristics of bearing signals has not been considered. In the absence of handling bias, the root cause for generalization errors cannot be clarified under various load conditions. This paper thus demonstrates that certain bias exists in diagnostic models for different loads of bearings, and the main factor of bias is impulsiveness. The existence of bias is shown with quantitative analysis by applying fairness criteria to diagnostic models. Also, qualitative analysis is conducted with gradient-weighted class activation mapping (Grad-CAM) for vibration signals of bearings, which proves that the large amplitude of impulse can be the source of bias. To correct this impulsiveness bias, a framework of fairness approach is newly proposed for bearing fault diagnosis under various loads. The process of correcting bias contains two steps: categorizing samples based on impulsiveness and training models with fairness criteria. Different from the previous domain adaptation-based approaches, the proposed method can achieve superior diagnostic performances by correcting bias that causes generalization errors. The effectiveness of the proposed method is validated with public-bearing datasets with various loads. The results show that the fairness approach can be the mainstream solution for fault diagnosis of rotary machines under different load conditions.

During the operation of rotating machinery, the vibration signals measured by sensors are the aliasing signals of various vibration sources, and they contain strong noises. Conventional signal processing methods have difficulty separating the aliasing signals, which causes great difficulties in the condition monitoring and fault diagnosis of the equipment. The principle and method of blind source separation are introduced, and it is pointed out that the blind source separation algorithm is invalid in strong pulse noise environments. In these environments, the vibration signals are first de-noised with the median filter (MF) method and the de-noised signals are separated with an improved joint approximate diagonalization of eigenmatrices (JADE) algorithm. The simulation results found here verify the effectiveness of the proposed method. Finally, the vibration signal of the hybrid rotor is effectively separated by the proposed method. A new separation approach is thus provided for vibration signals in strong pulse noise environments.

The broadcast nature of wireless channels leads to the developments and applications of physical layer security techniques. The Artificial Noise (AN) aided technique is proposed to interfere with the eavesdropper for secure communications. A suppression method called Joint Approximate Diagonalization of Eigen-matrices (JADE), which is based on blind source separation algorithm (BSS), is presented under this circumstance. The eavesdropper is able to separate the communication signal and the noise. Simulation results demonstrate that the proposed method is feasible under certain circumstances, which helps the eavesdropper to defeat the AN aided secure communications effectively.

In this chapter, an effective blind source separation (BSS) algorithm is applied to solve the co-channel interference problem in wireless communication systems. Algorithms developed for this purpose must not only have the capability of working in the complex domain and improving output signal to interference plus noise ratio (SINR), but also have relatively low computational complexity. We propose a fast Fourier transform (FFT)-based algorithm called feedback independent component analysis (FebICA) that is able to blindly separate complex modulated digital signals. By applying this algorithm to communication signals, it is observed that it has the advantages of SINR gain improvement as well as low computational complexity. The performance of the FebICA algorithm is shown to be better than the joint approximate diagonalization of eigen-matrices (JADE) algorithm in terms of the output SINR and requires lower computational complexity than the analytical constant modulus algorithm (ACMA). The algorithm is also shown to be more robust with increasing number of sources compared to other algorithms. The separation performance by using the collected field data has also been demonstrated.

Rotating machine fault diagnosis by a novel fast sparsity-enabled feature-energy-ratio method

Diagnosing faults in electric vehicles (EVs) is a great challenge. The purpose of this paper is to demonstrate the detection of faults in an electromechanical conversion chain for conventional or autonomous EVs. The information and data coming from different sensors make it possible for EVs to recover a series of information including currents, voltages, speeds, and so on. This information is processed to detect any faults in the electromechanical conversion chain. The novelty of this study is to develop an architecture for a fault diagnosis model by means of the feature extraction technique. In this regard, the long short-term memory (LSTM) approach for the fault diagnosis is proposed. This approach has been tested for an EV prototype in practice, is superior in accuracy over other fault diagnosis techniques, and is based on machine learning. An EV in an urban context is modeled, and then the fault diagnosis approach is applied based on deep learning architectures. The EV and the fault diagnosis model is simulated in Matlab software. It is also revealed how deep learning contributes to the fault diagnosis of EVs. The simulation and practical results confirm that higher accuracy in the fault diagnosis is obtained by applying the LSTM.

Conventional time-division duplex (TDD) orthogonal frequency division multiplexing (TDD-OFDM) system is vulnerable to the pilot spoofing attack as it mainly relies on employing the deterministic pilot to capture the channel state information (CSI). To solve the abovementioned issue, we in this paper propose an independent component analysis (ICA) based channel identification and estimation (ICA-CIE) mechanism which uses randomized pilot to paralyze the pilot spoofing attack. Specifically, we first convert the pilot spoofing attack to the pilot jamming attack by exploiting pilot randomization. Then, an eigenstructure-based detector (EID) is proposed to realize efficient pilot jamming attack detection. In particular, on one hand, Least Square (LS) method is adopted if no jamming attack is detected. On the other hand, if jamming attack is detected, we further design a channelseparation oriented joint approximate diagonalization of eigenmatrices (CS-JADE) algorithm for channel estimation and identification. Therein, an optimization problem is first formulated to optimize the fourth-order statistical information of the observation data, after which a pilot signal reconstruction scheme is further developed to help identify true sub-channels from the optimization output. Simulation results demonstrate that our proposed ICA-CIE mechanism can achieve highly-accurate channel estimation for the legitimate transceiver pair only by using 6 OFDM symbols within the coherence time. Moreover, we also observe that the estimation accuracy of our proposed mechanism is immune to Eve's transmit power, which further verifies superiority of our proposed mechanism.