Traditional device fault diagnostic methods in Industrial Internet of Things (IIoT) require nodes to upload local data to the cloud, which, however, may lead to privacy leakage issues. Although Federated learning (FL) methods can protect the privacy of data, many challenges still need to be addressed. For example, the nonindependently and identically distributed (non-IID) issue in FL prevents the convergence of global model. Moreover, FL lacks detection mechanism to resist poisoning attacks from malicious nodes, and it requires incentive mechanism to encourage nodes to share their resources. To address these challenges, this article proposes a secure and privacy-preserving FL system that leverages blockchain and edge computing technology. Specifically, a feature-contrastive loss function is constructed to train an unbiased global model under the non-IID condition. Additionally, a Byzantine-tolerance scoring mechanism is designed to resist poisoning attacks, and a reputation-based incentive algorithm is developed to estimate the rewards or penalties owed to nodes. The proposed method is applied to two case studies: 1) chiller fault diagnosis for heating, ventilation and air conditioning systems and 2) gearbox fault diagnosis for wind turbines in IIoT. Experimental results show the superior performance of the proposed method.

Solar Insecticidal Lamp Internet of Things (SIL-IoT) nodes are susceptible to failures due to the harsh environment, burn-in, theft, and vandalism in the agricultural setting. Most state-of-the-art research mainly focuses on fault detection without considering hardware and communication performance in terms of potential fault modes, energy consumption, and network loads. This study presents a completely decentralized solution, namely a SIL-oriented binary sliding window-based fault self-detection scheme (SILOS), which can be performed on each SIL-IoT node. The problem we are trying to answer is how to detect faults as accurately as possible while keeping the communication overhead, memory, and computational costs low. Specifically, we develop a fault dictionary concept to 1) model the faults of SIL-IoT nodes; 2) construct the fault dictionary according to the characteristics of measurements; and 3) detect faults via the fault dictionary. In addition, a binary-based sliding window (BSW) fault self-detection approach is proposed to save detection costs and reduce the false alarm rate (with only 92 B system caches). A series of experiments are performed to evaluate the performance of the proposed method. The result demonstrates that the BSW method can detect faults with an average accuracy of 99.14% with less than 1% energy consumption. By only sending the fault code, 71-B data (i.e., data transmitting and forwarding) can be reduced, saving energy consumption, and decreasing network congestion.

Solar insecticidal lamps Internet of Things (SIL-IoT) is a new green prevention and control technology for pest management. In the implementation of SIL-IoT to large-scale regions, two practical issues remain to be solved, that is: 1) scheduling the cleaning tasks of SILs periodically and 2) minimizing the insecticidal efficiency reduction over time. As smartphones are widely available among farmers across the globe, mobile crowdsensing (MCS) for agricultural data collection becomes a cost-effective and efficient solution by integrating participatory sensing based on a large group of individuals. This article proposes an MCS-enabled framework to address the SIL maintenance problem (SILMP) and perform system analysis by considering both the partition structure of farmland and the insecticidal efficiency of SILs. In addition, considering the farmland’s practical natural geographical features, we propose dividing the regions of interest into numerous subareas, where each subarea can be considered a separate partition. Finally, we formulate the SILMP framework as two subproblems, i.e., path planning and task selection, and propose two different methods to tackle each problem based on the concept of greedy algorithm. Simulation results show that our proposed methods have improved performance in the tradeoff between task cost and insecticidal efficiency and outperform the three selected baseline algorithms.

Permutation Entropy (PE) has been widely applied as a non-linear statistical indicator to estimate the change of complexity in time series. Though it is conceptually simple and computationally fast, PE encounters a few limitations. For example, the amplitude differences in time series are neglected, and the symbolic sequences generated from equal values are according to their emergence order. In this paper, an Edge Permutation Entropy (EdgePE) measure is proposed to improve the performance of PE, mainly overcoming its lack of ability to differentiate between amplitude differences in motifs that correspond to the same order pattern. The advantage of EdgePE relies on that amplitude change information can be identified and distinguished by the information underlying in the “edge” distance between data points in the reconstructed embedded vectors. To demonstrate its improvement, the proposed EdgePE is compared with other related improved PE approaches, for analyzing synthetic time series and experimental rolling bearing data sets, respectively. The results indicate that the EdgePE can effectively characterize amplitude changes in time series (e.g., for spike and stuck detection) and improve the accuracy of pattern recognition for rolling bearing fault diagnosis, compared to those of the other related PE measures.

Multiscale Permutation Entropy (MPE) has been applied as a non-linear measure for estimating the complexity of time series. Nevertheless, the coarse-grained procedure in MPE only takes low-frequency information into account. To overcome this shortcoming, in this paper, a new entropy measure, named Fine-to-Coarse Multiscale Permutation Entropy (F2CMPE), is proposed to provide stable and reliable results by offering both low-frequency and high-frequency information. Firstly, the F2C signals are created based on the reconstruction of selected wavelet coefficients using wavelet packet decomposition. Then, permutation entropy is used to estimate the complexity and dynamic change of the F2C signals. Experimental analysis is carried out to investigate and compare the performance of the proposed F2CMPE with that of the MPE. Results indicate that the proposed method can give consistent and stable entropy measure for rolling bearing fault diagnosis.

Fault diagnosis of incipient crack failure in rotating shafts allows the detection and identification of performance degradation as early as possible in industrial plants, such as downtime and potential injury to personnel. The present work studies the performance and effectiveness of crack fault detection by means of applying wavelet packet decomposition (WPD) and empirical mode decomposition (EMD) on fault diagnosis of rotating shafts using multiscale entropy (MSE). After WPD and EMD, the most sensitive reconstruction vectors and intrinsic mode functions (IMFs) are selected using Shannon entropy. Then, these feature vectors are fed into support vector machine (SVM) for fault classification, where the entropy features represent the complexity of vibration signals with different scales. Experimental results have demonstrated that WPD combined with MSE can achieve an accuracy of 97.3% for crack fault detection in rotating shafts, whilst EMD combined with MSE has shown a higher detection rate of 98.5%.

Fault diagnosis of roller bearings in rotating machinery is of great significance to identify latent abnormalities and failures in industrial plants. This paper presents a new self-adaptive fault diagnosis system for different conditions of roller bearings using InfraRed Thermography (IRT). In the first stage of the proposed system, 2-Dimensional Discrete Wavelet Transform (2D-DWT) and Shannon entropy are applied respectively to decompose images and seek for the desired decomposition level of the approximation coefficients. After that, the histograms of selected coefficients are used as an input of the feature space selection method by using Genetic Algorithm (GA) and Nearest Neighbor (NN), for the purpose of selecting two salient features that can achieve the highest classification accuracy. The results have demonstrated that the proposed scheme can be employed effectively as an intelligent system for bearing fault diagnosis in rotating machinery.

Industrial Wireless Sensor Networks (IWSNs) are expected to offer promising monitoring solutions to meet the demands of monitoring applications for fault diagnosis in large-scale petrochemical plants, however, involves heterogeneity and Big Data problems due to large amounts of sensor data with high volume and velocity. Cloud Computing is an outstanding approach which provides a flexible platform to support the addressing of such heterogeneous and data-intensive problems with massive computing, storage, and data-based services. In this paper, we propose a Cloud-based Data-intensive Framework (CDF) for on-line equipment fault diagnosis system that facilitates the integration and processing of mass sensor data generated from Industrial Sensing Ecosystem (ISE). ISE enables data collection of interest with topic-specific industrial monitoring systems. Moreover, this approach contributes the establishment of on-line fault diagnosis monitoring system with sensor streaming computing and storage paradigms based on Hadoop as a key to the complex problems. Finally, we present a practical illustration referred to this framework serving equipment fault diagnosis systems with the ISE.

As the compensation and extension of static wireless sensor nodes, wearable helmets can build regional early warning network of personnel security. In this paper, a wearable helmet is presented towards early warning of leaking toxic gas in large-scale petrochemical plants for protecting the lives and safety of workers better.

This paper focuses on using wearable equipment to make workers participate in constructing monitoring maps in large-scale petrochemical plants, which collaborates with static sensor nodes in given areas. Furthermore, this study provides a sensing pattern with less cost and higher flexibility, which effectively contribute to collective effort with static sensor nodes. Several open research issues are discussed.