The advent of equipment condition evaluation at the edge, facilitated by the Internet of Things (IoT), has led to significant reduction in data transmission and improvement in diagnostic efficiency. Nevertheless, the multi-scale components and noise interferences will seriously affect the quality of end-side sensed signals. Additionally, the mismatch between edge-side rigid models and time-varying nature of data features can lead to the failure of edge evaluation. To overcome these issues, this study introduces an IoT-based adaptive multiplication-convolution sparse denoising (AMCSD) method for the accurate equipment edge condition evaluation. Initially, from the perspective of signal processing synergized with deep learning, a lightweight signal denoising network is proposed with an array of multiplication filtering kernels (MFKs). Guiding by fault signal modulation mechanism, the learned MFKs sparse filters can adaptively extracted the fault-related frequency features with irrelevant components suppressed from the time-series differential information distributed in the degeneration process. Subsequently, an end-edge collaborative mechanism framework is designed and deployed on end-edge hardware unit. A compact end-side processing node (EPN) prototype can achieve efficient edge denoising effect with data adaptive compressing. Concurrently, the edge-side device named AlxBoard can implement the model adaptive dynamic updating. This means that adaptive signal denoising employed in the end-side focuses on the improvement of signal quality and sparse filter learning encompassed in the edge-side aims to solve the model mismatch. These advancements are anticipated to provide a monotonic but sensitive evaluation of degradation at the edge, surpassing the capability of conventional approaches.

Equipments regularly change working speeds during real-time production owing to process requirements. Applying deep learning models trained in a single speed domain straightforwardly to other unknown speed domains is extremely challenging single-domain generalization problem. Therefore, this article proposes a histogram matching mixup based sequential embedding network (HmmSeNet) for single-domain generalization of intelligent fault diagnosis under unknown speeds. HmmSeNet consists of four components: histogram matching mixup (HMM); sequential embedding (SE); separable convolution; and decision making. First, inspired by histogram matching and Mixup, the HMM data augmentation method is proposed. HMM is capable of synthesizing data with the same semantic information, but different distributions from a single source domain data during the training process, thus augmenting the source speed domain to the unknown speed domains. Then, SE utilizes trainable linear dimensional boosting to approximate the distribution between samples, which reduces the effect of sample amplitude distribution shifts caused by speed changes and allows the model to learn domain-invariant features. Finally, three layers of separable convolution and global average pooling are used to accomplish an accurate and robust recognition task. Experimental results on three datasets show that the proposed approach is only trained on a single speed domain, while it has good diagnostic performance on other unknown speed domains, even varying speed domains. The powerful generality and flexible deployment capability of HmmSeNet for speed changes are also demonstrated by ablation experimental analysis and dimensional analysis.

With the excellent capacity of feature representation and nonlinear mapping, deep learning with stacking deeply has aroused goer research interest in the field of intelligent fault diagnosis. However, under the case that mechanical failure signals, including gears, bearings, etc., essentially follow the excitation mechanism and modulation principle, an interpretable expression of deep learning architecture for intelligent diagnosis has been rarely discussed. Motivated by this issue, this study presents a novel interpretable multiplication convolution network (MCN), where three designed layers, including a feature separator, a feature extractor, and a classifier, are operated on spectrum samples input. Different from the conventional models, a series of multiplication filtering kernels (MFKs) are analytically designed to extract the differential modes from spectrum samples in an ex-ante interpretable way. The separated modes are stacked into a filtered mode map. A convolution layer is later used as the feature extractor to further abstract high-level feature representations. Finally, a dense decision layer is taken as the classifier for fault identification. Specially, to strengthen the sensing ability of MFKs, an anti-aliasing constraint is introduced to improve the information diversity of the separator. In essence, MCN operates in a novel framework collaborating signal processing with deep learning. Experimental results validate the effectiveness of the proposed MCN. Besides, feature map visualizations are further implemented to verify that the desired fault-sensitive modes in spectrum samples can be precisely mined, which provides the MCN with higher recognition accuracy and good ex-post interpretability. Benefiting from analytic kernel design, MCN has fewer model parameters as a lightweight efficient architecture, which shows enormous potential in the application of edge intelligent fault diagnosis. Related source codes can be available at: https://github.com/CQU-BITS/MCN-main.

Sensor nodes have been widely used for mechanical equipment condition monitoring, especially for enclosed metal structures. In these cases, it is important to achieve communication between the sensor nodes inside the enclosed metal structures and the outside receiver node. Conventional wired communication using cables will affect the structural integrity, and wireless communication using electromagnetic waves will be shielded by metal structures, so neither is desirable. Motivated by these issues, this article proposes a Lamb-waves-based sparse distributed penetrating communication (SDPC) system using the propagation characteristics of Lamb waves considered harmful in previous studies to accomplish wireless simultaneous data communication. Combining phase-shift keying and pulse position modulation mechanisms, a phase-position modulation (P-PM) technology is proposed to improve the communication rate. To overcome the intersymbol interference problem caused by the propagation characteristics of Lamb waves, a phase-position sparse reconstruction strategy is proposed for demodulation, which is more convenient and accurate compared with the conventional cross-correlation method. It should be noted that the maximum bit rate without bit error reaches 830.48 kbps via P-PM parameter optimization, which is 8.3 times higher than that ofon–offkeying modulation approach under the same experimental conditions. Finally, distributed communication experiments and simulations further verify the high accuracy, efficiency, and strong robustness of the proposed SDPC system, which can meet the communication requirements of enclosed metal structures.

Deep learning, with its ability of feature mining and logical judgement, has been widely studied in industrial intelligent diagnosis, including bearing fault diagnosis. However, an explicable and representable expression of deep learning architecture for the variational working conditions has been rarely discussed while it is known that vibration features from bearings are seriously influenced by variational working conditions. In this article, a deep interpolation ConvNet (DICN) architecture with three special layers, consisting of multiple sub-ConvNet units, weight unit, and fusion unit, is presented with the basic deep ConvNet architecture. Different from the traditional network, the first sub-ConvNet extracts the fault features under different working conditions, while the corresponding condition weight unit is learned from a working condition identification task. With the principle of interpolation theory, fusion unit is employed to achieve a sound fault feature representation under unknown working condition, which is named as ConditionSenseNet (CSN). This CSN architecture provides a way to dynamically express the crucial features hidden in the samples with the influence of working conditions suppressed, especially the variational working factors will be interpolated in this nonlinear fitting model. Additionally, three experimental studies are tested to verify the effectiveness of the proposed DICN method for bearing intelligent diagnosis under variational working conditions. The results and comparisons with other seven deep learning models show the proposed method shows outstanding robustness and higher accuracy where the accuracy of DICN is higher than the one of convolution neural network by more than 9% even if the working condition is variational.

The transient features caused by a local fault are of vital importance for bearing fault diagnosis in an intelligent industry. Due to the uncertainty of fault forms and nonstationarity of operating conditions, the fault feature distribution influenced by the physical dynamic response of actual defect is always complex and irregular with morphological differences. This will bring embarrassments for an accurate fault diagnosis. Motivated by this, a convolution sparse self-learning (CSSL) is proposed in this article to accomplish an adaptive feature enhancement. In the view of image sparse processing, the representation for desired morphological structures is promoted by a two-dimensional optimizing approach with manifold sensing. From a randomly selected fragment, the time-frequency manifold learning is first applied to mine the latent structures. The image entropy is then introduced to adaptively output the optimal one as a sensing kernel. Therewith, this kernel is used to operate a shift-invariant sparse analysis on raw time-frequency image. Combining this rebuilt image with the raw phase, an enhanced signal is finally synthesized. In this manner, the desired transient morphology can be automatically mined, which is consistent with the physical dynamic response. Practical defective bearing data are analyzed to illustrate the effectiveness of the proposed method. Specifically, a comparison further illustrates that the proposed CSSL is superior in the morphological transient features enhancement.