Xiaogang Deng

Deep stochastic configuration network (DSCN) has demonstrated great potential in supervised machine learning tasks (e.g. data regression and pattern classification) due to its advantages of efficient model training and adaptive structure determination. However, its application in industrial fault detection scenarios remains unexplored, where only one class of normal training data is available. To handle this issue, this paper presents a self-supervised DSCN Model, called Contrast Learning-assisted One-class DSCN (CL-ODSCN), for fault detection in complex industrial systems. It consists of four contributions. (1) A one-class DSCN fault detection framework is built based on the self-learning philosophy, where feature information enhancement module is designed between two consecutive feature extraction layers to capture rich feature information for effectively monitoring the anomalous behaviors. (2) For effective fault detection, a modified objective function using contrast learning is constructed for strengthening the model discriminative capability for abnormal samples. (3) To implement automatic node configuration, a supervisory mechanism for model training is presented. (4) A probabilistic fault explanation strategy is developed by combining the Shapley value method and the Bayesian inference theory to locate fault-related input variables. Two industrial cases, including a benchmark chemical system and a cementing pump unit, are used to validate the proposed model. The results demonstrate the CL-ODSCN model achieves better fault detection performance than the compared models.

With the application of unmanned forklifts in industries, industrial pallet recognition becomes an increasingly key role. However, real-time pallet recognition with high precision remains challenging due to adverse industrial environment and limited computational resources on embedded devices. To address it, an improved hierarchical lightweight model based on you only look once (YOLO) (named YOLO-HL) is proposed. The YOLO-HL introduces the discrete wavelet transform and learning feature for preserving texture details and fusing cross-stage features respectively. Both modules can reduce the convolution operation burden and extract effective features from poor-quality images. In addition, the YOLO-HL drops the large-object detection layer and adopts SIoU loss. It can further reduce the computational burden and improve the model accuracy. Experimental results show that the YOLO-HL achieves higher accuracy and needs less computational resource than the common YOLO models. It can also be deployed on embedded in-vehicle system effectively.

Plunger pumps serve as the core of the fluid delivery system and their fault diagnosis plays a critical role in ensuring system reliability and operational safety. As a prominent deep learning technique, convolutional neural networks (CNNs) have demonstrated significant success in the field of machinery fault diagnosis. However, conventional CNNs typically employ an end-to-end structure that directly processes raw measured signals as model input, often overlooking in-depth exploration of the underlying physical information and thus resulting in limited diagnostic accuracy under complex working conditions. To overcome this challenge, this paper presents a Physical information-assisted Attention Convolutional Neural Network (PACNN) for more robust and accurate plunger pump fault diagnosis. Unlike the traditional CNNs, which rely solely on original measured signals, PACNN enriches the network inputs by incorporating time-domain and frequency-domain features extracted from the measured signals. These features provide richer physical insights and are beneficial to enhancing the data representation. Furthermore, for handling the information fusion of the augmented network inputs, an improved region-focused multi-head attention mechanism is designed by accounting for the scale difference between sensor data and physical features. Additionally, the PACNN model incorporates a learnable convolutional kernel structure based on B-spline functions, thereby boosting the model’s capability to interpret complex fault patterns. Finally, an industrial plunger pump dataset is utilized to validate the method performance and the results show that the proposed PACNN achieves an average diagnostic accuracy of 99.50%, significantly outperforming traditional methods.

When incipient fault data of chemical processes is insufficient, transfer learning enables fault degradation trend prediction and process safety improvement. Traditional echo state network (ESN)‐based transfer learning generally utilizes a single source domain, while traditional multi‐source domain selection methods do not consider information complementation between source domains. Therefore, a multi‐source transfer learning based on ESN (MTL‐ESN) is proposed to improve chemical process time series prediction performance. In order to consider information complementation between source domains, a similar‐different source domain selection strategy is designed. To comprehensively utilize the transfer knowledge of multi‐source domains, transfer ESN sub‐models are established for selected source domains, and integrated to an ensemble ESN prediction model. For faults, especially mixed fault operating conditions, the proposed MTL‐ESN can select source domains correctly and predict variable trend accurately. Simulations on a continuous stirred tank reactor demonstrate the effectiveness of the proposed time series prediction method.

Stochastic configuration broad learning system (SCBLS) has demonstrated notable advantages in industrial soft sensing due to its computational efficiency and supervised node configuration. However, conventional SCBLS overlooks the dynamic characteristics of industrial data, which limits the prediction accuracy of soft sensor models. To address this limitation, this study proposes a two‐layer temporal SCBLS (TT‐SCBLS) for industrial soft sensor modelling. The proposed framework consists of two key modules: an output‐related dual‐layer temporal feature extraction module and an enhancement node stochastic configuration module. The former is used to build the primary mapped features, where the first layer employs quality‐related slow feature analysis (QSFA) to extract intrinsic slow features, mitigating the impact of data noise, and the second layer utilizes a cycle reservoir with regular jumps (CRJ) network to capture temporal dependencies in the process data. The later introduces the stochastic configuration algorithm for incremental node expansion at the enhancement layer, ensuring efficient model adaptation. Additionally, a kernel Shapley additive explanation (SHAP) model is integrated to quantify the contributions of input variables, enhancing model transparency. The proposed method is validated on two benchmark industrial systems, with experimental results confirming its superior prediction performance compared to existing approaches.

Cementing pumps are key equipment in oilfield well cementing operations. Diagnosing their running status is a crucial and challenging task. To handle this issue, this paper forwards a consistency-enhanced dual-weighted broad learning system (BLS) for effective cementing pump fault diagnosis. First, the vibration signals of cementing pump are analyzed to obtain the features of time and frequency domains. To make efficient use of the time series properties of signals, a phase space reconstruction technology is introduced to generate the augmented features. Second, from two distinct perspectives of variables and samples, a consistency-enhanced dual weighting strategy is designed to improve the basic BLS. The variable-dimension weights are constructed by the partial F test method to highlight the features contributing largely to the classification performance, whereas the sample-dimension weights are developed by the sample divergence analysis for emphasizing the key samples. To further suppress the noise influence and enhance the model’s robustness, a consistency constraint is introduced to the BLS optimization objective. Third, to provide a reasonable explanation of the decision result, an error gradient-based input attribution method is proposed to indicate which features are closely related to the classification results. Finally, a real cementing pump condition monitoring experimental platform is constructed and an application on the experimental dataset is used to validate the method’s performance. The application results demonstrate that the proposed method has a better fault classification accuracy than the basic BLS method and provides a reasonable explanation for the decision procedures.