Unsupervised cross-domain fault diagnosis of bearings has practical significance; however, the existing studies still face some problems. For example, transfer diagnosis scenarios are limited to the experimental domain, cross-domain marginal distribution and conditional distribution are difficult to align simultaneously, and each source-domain sample is assigned with equal importance during the domain adaptation process. Aiming at the above-mentioned challenges, this article proposes a novel joint transfer network for unsupervised bearing fault diagnosis from the simulation domain to the experimental domain. The sufficient bearing simulation data containing rich fault label information are used to construct the source domain to reduce the dependence on the resources of laboratory test rigs. An improved loss function embedded with joint maximum mean discrepancy is designed to achieve simultaneous alignments of marginal and conditional distributions across domains in unsupervised scenarios. A weight allocation mechanism for each source-domain sample is developed to suppress negative transfer. Two experimental datasets collected from laboratory test rigs are used as the target domains to validate the effectiveness of the proposed method. The results show that the proposed method is superior to other popular unsupervised cross-domain fault diagnosis methods.

Entropy, as a complexity measure, has been widely applied for time series analysis. One preeminent example is the design of machine condition monitoring and industrial fault diagnostic systems. The occurrence of failures in a machine will typically lead to non-linear characteristics in the measurements, caused by instantaneous variations, which can increase the complexity in the system response. Entropy measures are suitable to quantify such dynamic changes in the underlying process, distinguishing between different system conditions. However, notions of entropy are defined differently in various contexts (e.g., information theory and dynamical systems theory), which may confound researchers in the applied sciences. In this paper, we have systematically reviewed the theoretical development of some fundamental entropy measures and clarified the relations among them. Then, typical entropy-based applications of machine fault diagnostic systems are summarized. Further, insights into possible applications of the entropy measures are explained, as to where and how these measures can be useful towards future data-driven fault diagnosis methodologies. Finally, potential research trends in this area are discussed, with the intent of improving online entropy estimation and expanding its applicability to a wider range of intelligent fault diagnostic systems.

Condition monitoring and incipient fault diagnosis of rolling bearing is of great importance to detect failures and ensure reliable operations in rotating machinery. In this paper, a new multi-speed fault diagnostic approach is presented by using self-adaptive wavelet transform components generated from bearing vibration signals. The proposed approach is capable of discriminating signatures from four conditions of rolling bearing, i.e., normal bearing and three different types of defected bearings on outer race, inner race, and roller separately. Particle swarm optimization and Broyden-Fletche-Goldfarb-Shanno-based quasi-Newton minimization algorithms are applied to seek optimal parameters of Impulse Modeling-based continuous wavelet transform model. Then, a 3-D feature space of the statistical parameters and a nearest neighbor classifier are, respectively, applied for fault signature extraction and fault classification. Effectiveness of this approach is then evaluated, and the results have achieved an overall accuracy of 100%. Moreover, the generated discriminatory fault signatures are suitable for multi-speed fault data sets. This technique will be further implemented and tested in a real industrial environment.

Wireless sensor networks (WSNs) often consist of hundreds of sensor nodes that may be deployed in relatively harsh and complex environments. In views of hardware cost, sensor nodes always adopt relatively cheap chips, which make these nodes become error-prone or faulty in the course of their operation. Natural factors and electromagnetic interference could also influence the performance of the WSNs. When sensor nodes become faulty, they may have died which means they cannot communicate with other members in the wireless network, they may be still alive but produce incorrect data, they may be unstable jumping between normal state and faulty state. To improve data quality, shorten response time, strengthen network security, and prolong network lifespan, many studies have focused on fault diagnosis. This survey paper classifies fault diagnosis methods in recent five years into three categories based on decision centers and key attributes of employed algorithms: centralized approaches, distributed approaches, and hybrid approaches. As all these studies have specific goals and limitations, this paper tries to compare them, lists their merits and limits, and propose potential research directions based on established methods and theories.

With the continuing advancements in technologies (such as machine to machine, wireless telecommunications, artificial intelligence, and big data analysis), the Internet of Things (IoT) aims to connect everything for information sharing and intelligent decision-making. Swarm intelligence (SI) provides the possibility of SI behavior through collaboration in individuals that have limited or no intelligence. Its potential parallelism and distribution characteristics can be used to realize global optimization and solve nonlinear complex problems. This paper reviews representative SI algorithms and summarizes their applications in the IoT. The main focus consists in the analysis of SI-enabled applications to wireless sensor network (WSN) and discussion of related research problems in the WSN. Also, we concluded SI-based applications in other IoT fields, such as SI in UAV-aided wireless network. Finally, possible research prospects and future trends are drawn.

Fault detection and diagnosis are vital to identify incipient failures and avoid unexpected downtime in rotating machinery. This paper proposes a new rolling element bearing fault diagnosis method by integrating the Fine-to-Coarse Multiscale Permutation Entropy (F2CMPE), Laplacian Score (LS), and Support Vector Machine (SVM). Based on the constructed F2C signals using Wavelet Packet Decomposition (WPD), a novel entropy measure, namely F2CMPE, was proposed by calculating PE via multiple F2C signals. The F2CMPE method estimates the complexity changes of time series from both low and high-frequency components. Besides, the F2CMPE mitigates the drawback of producing time series with sharply reduced data length via the coarse-grained procedure in Composite Multiscale Permutation Entropy (CMPE). The comparative performance of the F2CMPE and CMPE is investigated by analyzing synthetic and experimental signals for bearing diagnosis. In the proposed bearing diagnosis method, F2CMPE is first used to extract entropy features from bearing vibration signals. Then, LS and SVM are used for feature selection and fault classification respectively. Finally, the effectiveness of the proposed method is verified for rolling bearing fault diagnosis using experimental vibration data sets, and the results have demonstrated the proposed method’s ability to recognize and identify bearing fault patterns under different fault states and severity levels.