Inverter open-circuit faults can cause significant changes in the output current of permanent magnet synchronous motor (PMSM), so many fault diagnosis methods based on convolutional neural networks take the current as monitor signals. However, there are some shortcomings in the application of convolutional neural networks for fault diagnosis. On the one hand, most of the traditional pooling methods confuse the different frequency components of the feature map due to ignoring the classical sampling theorem, which decreases the accuracy of fault diagnosis. On the other hand, the generalization performance of the diagnostic model is not good enough when the data distribution changes due to variations in the operating conditions of the drive system. To address these issues, this paper proposes an open-circuit fault diagnosis method using an adaptive sparse attention wavelet network (ASAWN), where a novel pooling layer, termed as adaptive sparse attention wavelet pooling (ASAW-pooling) layer, is designed using discrete wavelet transform and adaptive sparse attention mechanism. The pooling layer improves the robustness of the diagnostic model by filtering out the high-frequency components of the current data and the redundant information of the attention mechanism. Experimental results show that the proposed method can effectively diagnose 22 types of open-circuit faults. Besides, a comparison with other models shows that the proposed model not only has high accuracy but also performs strong robustness.

Fingerprinting based on Wi-Fi Received Signal Strength Indicator (RSSI) has been widely studied in recent years for indoor localization. While current algorithms related to RSSI Fingerprinting show a much lower accuracy than multilateration based on time of arrival or the angle of arrival techniques, they highly depend on the number of access points (APs) and fingerprinting training phase. In this paper, we present an integrated method by combining the deep neural network (DNN) with improved K-Nearest Neighbor (KNN) algorithm for indoor location fingerprinting. The improved KNN is realized by boosting the weights on K-nearest neighbors according to the number of matching access points. This will overcome the limitation of the original KNN algorithm on ignoring the influence of the neighboring points, which directly affect localization accuracy. The DNN algorithm is first used to classify the Wi-Fi RSSI Fingerprinting dataset. Then these possible locations in a certain class are also classified by the improved KNN algorithm to determine the final position. The proposed method is validated inside a room within about 13⁎9 m2. To examine its performance, the presented method has been compared with some classical algorithms, i.e., the random forest (RF) based algorithm, the KNN based algorithm, the support vector machine (SVM) based algorithm, the decision tree (DT) based algorithm, etc. Our real-world experiment results indicate that the proposed method is less dependent on the dense of access points and indoor radio propagation interference. Furthermore, our method can provide some preliminary guidelines for the design of indoor Wi-Fi test bed.

Gear fault diagnosis is an important part of rotating machinery maintenance. Most state-of-the-art research methods in the field of gear fault diagnosis combine deep learning methods with multisensor data fusion, big data analysis, and physical models to achieve more accurate fault diagnosis techniques; however, there are still some shortcomings in the method of gear fault diagnosis based on deep network models. First, the impact of noise on model diagnostics is rarely considered, and second, there is a conflict between a large number of parameters and calculations of deep networks and the computing resources of current embedded platforms. To address these issues, this article proposes a multilayer fusion convolutional neural network (AMFCNN) and a multilayer fusion module-relational knowledge distillation (MFM-RKD) module based on an attention mechanism. The multilayer fusion network uses an inception module to extract multiscale features from multisensor information and adopts an attention mechanism to extract features from the network in different periods. The MFM-RKD module activates the extracted features of different periods through a fully connected layer and transfers structural knowledge to a lightweight student network through RKD to build a lightweight multisensor gear fault diagnosis model. Experiments show that the lightweight model has excellent robustness in different noise environments. Compared with the teacher network, the model performance is reduced by less than 2%, while the calculations of the model are reduced by 92 times and the running memory is reduced by 7.74 times, which provides an efficient solution to address the robustness and model deployment issues in gear fault diagnosis.

A localization system is one of the basic requirements for coal mines. Ultra-wideband (UWB), as a technology with broad application prospects, is considered to have great potential in the absence of satellite signals, especially in the underground mine environment, as it can provide rescue assistance. However, state-of-the-art UWB position systems in coal mines cannot efficiently differ the line-of-sight from all communication links, which results in deterioration of the localization accuracy. In this paper, we propose a LOS/NLOS classification method based on a deep learning algorithm. Specifically, we utilize the Generative Adversarial Networks (GAN) to generate diagnostic data for frame transmission under non-line-of-sight (NLOS) condition. Then, a Convolutional Neural Network (CNN) is adopted to identify the NLOS communication. Finally, the trilateral centroid positioning algorithm (TCPA) based on ranging data is used to verify the effect of our method for a localization system in coal mines. Field experiments show that our method can accurately differ the LOS/NLOS with the accuracy of 91.19%. The TCPA algorithm with our method can obtain 3.11% improvement compared with the scenario without using our method.

Device-free localization (DFL) is a technique of locating targets via wireless sensor networks (WSNs) without carrying any electronic devices. This article introduces a device-free vehicle localization system that can determine the position and 2-D size of a dynamic vehicle in a monitoring region via ultra wide band (UWB) networks. Specifically, our system involves an obstructed recognition model for detecting the state of links, a state-input radio tomography imaging (SI-RTI) algorithm for reconstructing a high-resolution image, a contour threshold method for predicting the size of a vehicle target, and a gradient-advance-retreat (GAR) optimization algorithm for improving accuracy. Field experiments show that our system can estimate the position and size of a moving vehicle with a location root-mean-square error (RMSE) of less than 1.1 m and a root-mean-square overlap degree of more than 75% between the prediction and the real target.

The gear fault diagnosis technology based on the signal is a crucial for maintaining the normal operation of gear in the motor drive chain. In some cases, it is challenging to add sensors on the unit of the motor transmission chain for collecting vibration signals in practical engineering applications. However, the current signal can be collected. Nonetheless, due to the long distance between the collection point and the fault source, it becomes difficult to extract the features of the weak gear fault from the current signal. In order to solve the aforementioned problems efficiently, an optimized principal neighborhood aggregation (OPNA) graph neural network was proposed to diagnose gear faults in the motor drive chain. Firstly, the current signal is reconstructed to obtain the topological data graph sample by the graph sample construction method proposed in this paper. Secondly, OPNA, an architecture that combines multiple message aggregators with a degree scaler, was designed to extract the features of nodes and edges. Subsequently, the embedding and the particular pooling improvement were used to reduce the number of nodes and achieve steady and rapid classification. Finally, the experimental studies, based on the current signal of gear data set, were conducted to validate the effectiveness of the proposed method and its superiority over the traditional methods.