Incremental learning (IL) provides a dynamic framework for expanding the classification capacity of data-driven systems, thereby facilitating unknown fault diagnosis (UFD) in motor systems. However, the necessity for a manually established training set with unknown fault samples for the retraining of IL models, combined with insufficient consideration of real time, presents significant challenges. To address these challenges, this article proposes an automatic IL method based on edge computing (AILEC) for UFD of motors. First, the method introduces a convolutional encoder based on a training guide-separation module (CETGM) and a feature similarity match (FSM) technique. These components are designed to function effectively at the edge after initial training. An IL method, based on edge joint training (EJT), is then proposed to extend the classifiable quantity of the model at edge end, based on UFD results derived from CETGM and FSM. The superiority of the proposed method is validated through experiments on a motor test rig. The results demonstrate that the approach achieves 99.99% accuracy for UFD, with an average accuracy of 98.84% across 4 to 10 incremental states. Additionally, the system delivers a model size of 0.5 MB, an inference time of 2.09 ms, and a model update time of 164 s. The proposed method outperforms several existing approaches in terms of accuracy and real-time processing capabilities. It provides an intelligent solution for UFD of motors, featuring continuous model updates and real-time IL.

Short-term electric load forecasting is essential for efficient power system operation, but existing deep learning models struggle to capture the multi-scale features and cyclical fluctuations inherent in short-term load data. This paper introduces a novel deep learning model, Wavelet Transform Convolution-inverted ProbSparse Transformer (WTC-iPST), specifically designed for short-term load forecasting. Unlike existing deep learning models, WTC-iPST leverages Wavelet Transform Convolution (WTConv) for multi-scale feature extraction and integrates Wavelet Kolmogorov-Arnold Networks (Wav-KAN) to enhance the ProbSparse self-attention mechanism, significantly improving the model's ability to capture multi-scale features and cyclical fluctuations inherent in short-term load data. This design addresses the challenge of extracting multi-scale and cyclical features from short-term load data, which existing models struggle with, and strengthens the model's capacity to handle long series. Additionally, WTC-iPST incorporates quantile regression to quantify uncertainty and provide confidence intervals, further enhancing the prediction's reliability and accuracy. Experimental results on real-world datasets demonstrate that WTC-iPST outperforms state-of-the-art forecasting models, with significant improvements over the baseline iTransformer, achieving reductions of up to 16.84% in RMSE, 18.09% in MAPE, and 17.65% in RRMSE, as well as an increase of up to 2.96% in R². In terms of probabilistic prediction, WTC-iPST consistently maintains a narrow confidence interval with high interval coverage. Moreover, WTC-iPST shows strong performance across various prediction horizons and different distribution substations, highlighting its robustness and adaptability. These results confirm that WTC-iPST provides more accurate and reliable forecasts, making it a valuable tool for power system dispatch and operational planning.

To detect the demagnetization fault (DF) of a dual-sided permanent magnet synchronous linear motor, a new method based on deep feature fusion of external stray flux signal (ESFS) is proposed. First, finite element models under ideal materials and assembly conditions are established to extract ESFS to reflect DF information. Second, Markov transition field and recurrence plot transform 1-D signals into 2-D images, to realize DF feature visual enhancement. Low-rank representation networks can merge the advantages of both methods by image fusion. Then, an accurate diagnosis framework, as efficient channel attention-MobileNetV3, is proposed to conduct deep feature extraction and realize diagnosis in both qualitative fault type classification and fault degree evaluation aspects. The classification accuracy reaches 98.50%, and the evaluation indexR2reaches 0.96, superior to other frameworks. Finally, a tunnel magnetoresistance sensor is applied to realize ESFS noninvasive online measurement, and an experimental platform is built to certify the superiority.

Bearings are crucial components of rotating machines, and accurately predicting their remaining useful life is paramount for ensuring machine safety and maintenance. Existing prognostic studies predominantly rely on limited monitoring data collected under specific operating conditions for modeling, often overlooking valuable degradation characteristics contained under other conditions. To tackle these challenges, this study proposes a federated learning (FL)-based prognostic method that aims to collaboratively construct personalized prognostic models for bearings operating under different conditions within the FL framework. Specifically, a client selection strategy is initially adopted to identify clients with closely related high-level degradation features, guiding effective aggregation among relevant clients. This strategy significantly improves the accuracy and convergence of the prediction model. Subsequently, based on the obtained similarity parameters, a personalized aggregation enhancement scheme is proposed to aggregate models in the subgroup of selected clients, further enhancing the prognostic performance of the prediction model. This study represents a novel attempt at constructing personalized prognostic models in scenarios involving data heterogeneity. Experimental results on two bearing data sets jointly verify the improved accuracy and convergence of the proposed method.

Edge computing is an emerging paradigm that offloads the computations and analytics workloads onto the Internet of Things (IoT) edge devices to accelerate the computation efficiency, reduce the channel occupation of signal transmission, and reduce the storage and computation workloads on the cloud servers. These distinct merits make it a promising tool for IoT-based machine signal processing and fault diagnosis. This article reviews the edge computing methods in signal processing-based machine fault diagnosis from the aspects of concepts, state-of-the-art methods, case studies, and research prospects. In particular, the lightweight designed algorithms and application-specific hardware platforms of edge computing in the typical fault diagnosis procedures, including signal acquisition, signal preprocessing, feature extraction, and pattern recognition, are reviewed and discussed in detail. The review provides an insight into the edge computing framework, methods, and applications, so as to meet the requirements of IoT-based machine real-time signal processing, low-latency fault diagnosis, and high-efficient predictive maintenance.

Induction motors (IMs) are used extensively as driving actuators in electric vehicles. Motor rotors are prone to defects in the die casting procedure, which can significantly reduce the production quality. Benefitting from the development of Internet of Things (IoT) techniques and edge computing, this study designed an instrumentation system for the fast inspection of rotor defects to meet the objectives of efficient and high-quality rotor production. First, an electromagnetic sensing device is designed to acquire the induced voltage signal of the rotor under investigation. Second, a residual multiscale feature fusion convolutional neural network model is designed to extract the hierarchical features of the signal, to facilitate defect recognition. The developed algorithm is deployed into a cost-effective edge computing node that includes a signal acquisition circuit and a Raspberry Pi microcontroller. The conducted experimental studies show that this implementation can achieve an inference time of less than 200 ms and accuracy of more than 99%. It is shown that the designed system exhibits superior performance when compared with conventional methods. The developed, compact and flexible handheld solution with enhanced deep learning techniques shows outstanding potential for use in real-time rotor defect detection.

Convolutional neural networks (CNNs) have been widely applied to motor fault diagnosis. However, to obtain high recognition accuracy, massive training data are typically required and transmitted to the cloud/local server for training, which may suffer from security and privacy problems. In this article, a noise-boosted CNN (NBCNN) model is developed to achieve accelerated training and improved recognition accuracy with limited training samples. First, the NBCNN model with a noise-injection fully connected layer is established. Then, a strategy for noise selection and injection is proposed to obtain an optimal matching among the data, model, and noise. Finally, the optimal injected noise accelerates the convergence of model training and improves the accuracy of motor fault diagnosis. Compared with the conventional CNN without noise injection and the state-of-the-art models, the effectiveness and superiority of the proposed NBCNN model are validated by two benchmark datasets. In addition, the algorithm is deployed onto an edge device and the results show that the training speed of the developed NBCNN can reach nine times faster than the conventional CNN. The proposed method shows remarkable potential for distributed model training, federal learning, and real-time motor fault diagnosis.

A multisensor data fusion method is investigated for bearing fault diagnosis of a switched reluctance motor (SRM) of an electric vehicle (EV) powertrain under varying speed conditions. The accumulative rotation angle of the SRM rotor is estimated by fusing the synchronous sampled current and vibration signals. The time-domain vibration signal is then resampled on an angular domain, and the bearing fault type is identified on the envelope spectrum of the resampled signal. In this article, an experimental setup is designed to validate the performance of the proposed method compared with the traditional ones. The practical EV working conditions including driving, coasting, and braking are considered in the experiments. Results indicated that the proposed method successfully diagnoses the SRM bearing faults under random and complex conditions. The method is promising for online SRM fault diagnosis under varying speed conditions as it requires no extra tachometer, specifically when the sensorless control strategy is adopted.

The die cast rotor bars in production squirrel cage induction motors (SCIMs) are easily subjected to porosity or other defects in production, which considerably affects the motors’ reliability and efficiency in operation. Planar flux sensing coils have been investigated for the defect detection of SCIM rotor. However, these types of sensors cannot accurately evaluate the severity of porosity or broken bar. This article develops a novel instrument to inspect and quantitatively analyze the rotor quality of SCIM. The sensor consists of the electromagnetic flux sensing coils directly from an SCIM stator. By injecting a dc voltage at phases A and B of the sensor, the induced voltage signal is generated from phase C. A quantitative fault indicator (QFI) is constructed on the basis of the instrument voltage output. The variation trend of the QFI with respect to fault severity is investigated by establishing a theoretical sensor model. Experimental results indicate that the proposed method can accurately detect the porosity and broken bar, and evaluate their severities for the die cast rotor. The developed solution can be easily implemented with low cost and computational complexity, which can achieve real-time inspection of SCIM rotor in theline.

The existing fault diagnosis methods of rotating machinery constructed with both shallow learning and deep learning models are mostly based on vibration analysis under steady rotating speed. However, the rotating speed frequently changes to meet practical engineering needs. The shallow learning models largely depend on domain experience of feature extraction, and training a deep learning model requires large samples and a long time. In addition, vibration monitoring has the shortcomings of contact measurement, small coverage, and noise interference. To address these problems, this article proposes a new fault diagnosis method with the least square interactive support matrix machine (LSISMM) and infrared thermal images. In this method, a novel matrix-form classifier called LSISMM is constructed under the concept of nonparallel interactive hyperplanes to fully leverage the structure information of infrared thermal images. To improve the computation efficiency, a new least square loss constraint is designed for LSISMM. Besides, we derive an effective solution framework based on the alternating direction method of the multiplier (ADMM) framework. The constructed LSISMM is directly used to analyze the collected thermal images of rotating machinery under time-varying speeds. Experiment results demonstrate that the proposed method is superior to state-of-the-art methods in terms of diagnosis accuracy and efficiency, especially under small thermal image samples.

Digital signal processing algorithms are widely adopted in motor bearing fault diagnosis. However, most algorithms are developed on desktop platforms, and their focus is on the analysis of offline captured signals. In this paper, a simple and easily implemented algorithm running on an embedded system is proposed for the online fault diagnosis of motor bearing. The core part of the algorithm is a stochastic-resonance-based adaptive filter that realizes signal denoising and adaptation of the filter coefficient. Processed by the filter, the period of the purified signal is obtained, and then the fault type of the motor bearing is identified. The proposed method has distinct merits, such as low computational cost, online implementation, contactless measurement, and availability for various speed motors. This paper provides a simple, flexible, and effective solution for conducting motor bearing diagnosis on an embedded/portable device. The algorithm proposed is validated by a brushless dc motor and a brushed dc motor fabricating with defective/healthy support bearings.