Wireless Sensor Network Simulation for Fault Detection in Industrial Processes

Decentralized adaptively weighted stacked autoencoder-based incipient fault detection for nonlinear industrial processes

In this study, a system for faults detection using a combination of Support Vector Data Description (SVDD) with metaheuristic algorithms is presented. The presented approach is applied to a real industrial process where the set of measured faults is scarce. The original contribution in this work is the industrial context of application and the comparison of swarm intelligence algorithms to optimize the SVDD hyper-parameters. Four recent metaheuristics are compared hereby to solve the corresponding optimization problem in an efficient manner. These optimization techniques are then implemented for fault detection in a multivariate industrial process with non-balanced data. The obtained numerical results seem to be promising when the considered optimization techniques are combined with SVDD. In particular, the Spotted Hyena algorithm outperforms other metaheuristics reaching values of F1 score near 100% in fault detection.

The self-attention mechanism has advantages in analyzing the internal characteristics of data and capturing local information. Therefore, it is applied to fault detection. Since complex industrial processes usually contain a mixture of properties, such as nonlinearity, dynamics, and non-Gaussianity, this places higher demands on fault detection methods. To further improve the performance of the self-attention mechanism for fault detection in complex industrial processes, an innovative self-attention mechanism integrating the adaptive double subspace (ISA-ADS) method is proposed for fault detection. First, the query matrix, the key matrix, and the value matrix of the self-attention mechanism are designed so that they can better focus on local information through the adaptive sample weight allocation strategy. At the same time, this design reduces the interference of nonlinear and noisy information and amplifies the impact of important fault information in the sample. Second, considering the problem of Gaussian and non-Gaussian distributions of the data and the different degrees of influence of different subspaces on the final process monitoring results, an adaptive Gaussian and non-Gaussian double subspace fault detection model based on the self-attention output matrix is established, which adaptively assigns different weights to different subspaces by determining their importance. The statistics of different subspaces are fused using the Bayesian inference and a new adaptive monitoring statistic is constructed to obtain a more accurate monitoring of the process state. Finally, the ISA-ADS is validated using the Tennessee Eastman (TE) process and the actual polyethylene production process. The results show that ISA-ADS has a low false alarm rate and a high fault detection rate, verifying the effectiveness of its fault detection performance.

Manifold regularized stacked autoencoders-based feature learning for fault detection in industrial processes

Wireless industrial sensor networks are wireless sensor networks which have been adapted to industrial applications. Most techniques for wireless sensor networks can be applied to wireless industrial sensor networks. However, for industrial applications of wireless industrial sensor networks, new requirements such as real-time, reliable delivery need to be considered. In this paper, we propose EARQ, which is a novel routing protocol for wireless industrial sensor networks. It provides real-time, reliable delivery of a packet, while considering energy awareness. In EARQ, a node estimates the energy cost, delay and reliability of a path to the sink node, based only on information from neighboring nodes. Then, it calculates the probability of selecting a path, using the estimates. When packet forwarding is required, it randomly selects the next node. A path with lower energy cost is likely to be selected, because the probability is inversely proportional to the energy cost to the sink node. To achieve real-time delivery, only paths that may deliver a packet in time are selected. To achieve reliability, it may send a redundant packet via an alternate path, but only if it is a source of a packet. Experimental results show that EARQ is suitable for industrial applications, due to its capability for energy efficient, real-time, reliable communications.

Recently, principal component analysis (PCA) and independent component analysis (ICA) have attracted increasing attention for fault detection in industrial processes. However, industrial processes usually have multimodal characteristics, in which case, the performance of traditional PCA and ICA methods is poor. A fault detection method based on the local entropy double subspace (LEDS) is proposed that effectively supervises the multimodal process with multiple steady modes. The proposed method uses the local probability density (LPD) to eliminate the multimodal characteristics of the data. Furthermore, local entropy (LE) is applied to mine the statistical information associated with the process data. The process variables are classified into Gaussian and non-Gaussian subspaces by the Kolmogorov–Smirnov (KS) test. PCA and ICA models are built in Gaussian and non-Gaussian subspaces, respectively, for fault detection. A Bayesian decision method is employed to transform the detection results into fault probabilities, and the detection results are combined with the final statistical information. This method effectively eliminates the multicentre structure of the data and improves the fault detection performance of traditional PCA and ICA. The proposed method is applied to the Tennessee Eastman multimodal process. The simulation results show that the fault detection rate of LEDS is better than the rates of traditional methods when the false alarm rate is low for most faults.

Long–short-term memory encoder–decoder with regularized hidden dynamics for fault detection in industrial processes

With the success of wireless technologies and the number of wireless devices increasing, the wireless coverage footprint expands and removes the needs of wired networks for industrial applications. Wireless industrial sensor networks (WISNs) are a type of wireless sensor networks (WSNs), which dedicate to industrial applications. The WISNs bring several advantages over traditional wired industrial networks for detection and control, include flexibility, scalability, remote maintenance, and rapid deployment. However, some technical issues and routing design principles need to be noticed in terms of the stringent requirements of industrial applications. In this paper, we firstly introduce the characteristics and some routing issues of WISNs. Then we discuss existing routing protocols for WISNs and analysis their performances. Finally, we outline several future research directions.

Design teacher and supervised dual stacked auto-encoders for quality-relevant fault detection in industrial process

Modern industrial plants generally consist of multiple manufacturing units, and the local correlation within each unit can be used to effectively alleviate the effect of spurious correlation and meticulously reflect the operation status of the process system. Therefore, the local correlation, which is called spatial information here, should also be taken into consideration when developing the monitoring model. In this study, a cascaded monitoring network (MoniNet) method is proposed to develop the monitoring model with concurrent analytics of temporal and spatial information. By implementing convolutional operation to each variable, the temporal information that reveals dynamic correlation of process data and spatial information that reflects local characteristics within individual operation unit can be extracted simultaneously. For each convolutional feature, a submodel is developed and then all the submodels are integrated to generate a final monitoring model. Based on the developed model, the operation status of the newly collected sample can be identified by comparing the calculated statistics with their corresponding control limits. Similar to the convolutional neural network (CNN), the MoniNet can also expand its receptive field and capture deeper information by adding more convolutional layers. Besides, the filter selection and submodel development in MoniNet can be replaced to generalize the proposed network to many existing monitoring strategies. The performance of the proposed method is validated using two real industrial processes. The illustration results show that the proposed method can effectively detect process anomalies by concurrent analytics of temporal and spatial information.

With the continuous evolution of modern industrial technology, industrial production has grown progressively complex, necessitating the use of various sensors to measure multiple process variables. However, intricate temporal dependencies and nonlinear relationships between data presented by multivariate sequences pose significant challenges to process fault detection. In response to these challenges, this paper proposes an attention-based gated recurrent unit autoencoder with skip connection (SAGRU-AE) model for monitoring large-scale, nonlinear, and multivariate industrial process faults. SAGRU-AE combines gated recurrent units, multi-head self-attention, and autoencoder to extract features from multivariable time series data efficiently. Concurrently, feature reuse is achieved through the skip connection structure, which improves the accuracy of data reconstruction. Based on the implementation of process data feature extraction and input reconstruction in SAGRU-AE, two statistics have been developed, namely the H2 statistic and square prediction error (SPE) statistic, for fault detection tasks. Ultimately, the feasibility and effectiveness of the proposed algorithms are validated through experimentation on the TE process.

In this work, we introduce MOLA, a multi-block orthogonal long short-term memory autoencoder paradigm, to conduct accurate, reliable fault detection of industrial processes. To achieve this, MOLA effectively extracts dynamic orthogonal features by introducing an orthogonality-based loss function to constrain the latent space output. This helps eliminate the redundancy in the features identified, thereby improving the overall monitoring performance. On top of this, a multi-block monitoring structure is proposed, which categorizes the process variables into multiple blocks by leveraging expert process knowledge about their associations with the overall process. Each block is associated with its specific orthogonal long short-term memory autoencoder model, whose extracted dynamic orthogonal features are monitored by distance-based Hotelling’s T2 statistics and quantile-based cumulative sum (CUSUM) designed for multivariate data streams that are nonparametric and heterogeneous. Compared to having a single model accounting for all process variables, such a multi-block structure significantly improves overall process monitoring performance, especially for large-scale industrial processes. Finally, we propose an adaptive weight-based Bayesian fusion (W-BF) framework to aggregate all block-wise monitoring statistics into a global statistic that we monitor for faults. Fault detection speed and accuracy are improved by assigning and adjusting weights to blocks based on the sequential order in which alarms are raised. We demonstrate the efficiency and effectiveness of our MOLA framework by applying it to the Tennessee Eastman process and comparing the performance with various benchmark methods.

In today's competitive industry marketplace, the companies face growing demands to improve process efficiencies, comply with environmental regulations, and meet corporate financial objectives. Given the increasing age of many industrial systems and the dynamic industrial manufacturing market, intelligent and low-cost industrial automation systems are required to improve the productivity and efficiency of such systems. The collaborative nature of industrial wireless sensor networks (IWSNs) brings several advantages over traditional wired industrial monitoring and control systems, including self-organization, rapid deployment, flexibility, and inherent intelligent-processing capability. In this regard, IWSN plays a vital role in creating a highly reliable and self-healing industrial system that rapidly responds to real-time events with appropriate actions. In this paper, first, technical challenges and design principles are introduced in terms of hardware development, system architectures and protocols, and software development. Specifically, radio technologies, energy-harvesting techniques, and cross-layer design for IWSNs have been discussed. In addition, IWSN standards are presented for the system owners, who plan to utilize new IWSN technologies for industrial automation applications. In this paper, our aim is to provide a contemporary look at the current state of the art in IWSNs and discuss the still-open research issues in this field and, hence, to make the decision-making process more effective and direct.

The industrial wireless sensor network (IWSN) is a surface-type of wireless sensor network (WSN) that suffers from high levels of security breaches and energy consumption. In modern complex industrial plants, it is essential to maintain the security, energy efficiency, and green sustainability of the network. In an IWSN, sensors are connected to the Internet in a non-monitored environment. Hence, non-authorized sensors can retrieve information from the IWSN. Therefore, to ensure that data access between sensors remains sustainable and secure, energy-efficient authentication and authorization are required. In this article, a novel Quantum Readout Gradient Secured Deep Learning (QR-GSDL) model is proposed to ensure that only trustworthy sensors can access IWSN data. The major objective of this QR-GSDL model is to create secure, energy-efficient IWSN to attain green sustainability and reduce the industrial impact on the environment. First, using the quantum readout and hash function, a registration method is designed to efficiently perform the registration process. Next, a gradient secured deep learning method is adopted to implement the authentication and authorization process in order to ensure energy-saving and secure data access. Simulations are conducted to evaluate the QR-GSDL model and compare its performance with that of three well-known models: online threshold anomaly detection, machine learning-based anomaly detection, and dynamic CNN. The simulation outcomes show that the proposed model is secure and energy-efficient for use in the IWSN. Moreover, the experimental results prove that the QR-SGDL model outperforms the existing models in terms of energy consumption, authentication rate, authentication time, and false acceptance rate.

In Industrial Wireless Sensor Networks (IWSNs), Sybil attacks compromise network topology and reduce data reliability by forging virtual nodes, leading to degraded network performance and significantly diminished monitoring accuracy. To address these issues, this study aims to propose a high-accuracy and highly robust Sybil attack detection method to overcome the limitations of traditional detection approaches, such as low precision and difficulty in handling ambiguous probability boundaries. The research designs a collaborative detection mechanism that integrates a CNN-BiLSTM-Attention (CBSA) deep learning module with the K-means clustering algorithm. By combining "multidimensional feature extraction via deep learning + clustering-based classification boundary optimization," an end-to-end Sybil attack detection model (CBSA-Kmeans) is constructed.The specific implementation includes four parts: 1. A Convolutional Neural Network (CNN) processes the raw sensor data matrix to extract spatial local patterns and capture abnormal correlation features among nodes. 2. A Bidirectional Long Short-Term Memory network (BiLSTM) processes the feature sequences output by the CNN. The forward LSTM learns the "past-present" temporal dependencies to identify the cumulative effects of attacks, while the backward LSTM models the "present-past" temporal correlations to trace attack origins. 3. An Attention mechanism is introduced to dynamically focus on key time steps corresponding to critical attack features, generating a weighted context vector and outputting attack probability predictions. 4. The K-means clustering algorithm is employed to perform secondary partitioning on the prediction probability space output by the CBSA module. By measuring Euclidean distances, high-density attack clusters and normal data clusters are constructed to form decision regions, thereby optimizing classification boundaries.Through a progressive approach of "spatial feature extraction → temporal dependency modeling and key feature enhancement → probability space clustering optimization," the model achieves attack detection: CNN first performs preliminary spatial feature screening, BiLSTM and Attention collaboratively mine temporal attack features and highlight critical information, and finally, K-means clusters the prediction probabilities to clarify the boundaries between attack and normal data. Experimental results demonstrate that the CBSA-Kmeans model excels in IWSN Sybil attack detection tasks: it achieves a detection accuracy of 98.2% and a recall rate of 96.7%, representing an improvement of over 12% compared to traditional detection methods. Additionally, the model has minimal negative impact on network performance, increasing IWSN network throughput by 23.5% and reducing data transmission latency by 31.8%, while effectively addressing the ambiguous probability boundary issue present in traditional methods. In conclusion, the CBSA-Kmeans model achieves high-precision and highly robust detection of Sybil attacks in IWSNs through the synergistic integration of deep learning and clustering algorithms, validating the effectiveness and superiority of this collaborative detection mechanism. This method provides a practical technical solution for IWSN security protection, ensuring network topology integrity and data transmission reliability while enhancing operational efficiency and monitoring accuracy. It holds significant practical application value for ensuring the secure and stable operation of wireless sensor networks in industrial settings.