In this paper a method for fault detection of Brushless Direct Current (BLDC) motors is proposed simply based on hall sensors that exist in most BLDC motors. Two important faults in these motors are bearing faults and demagnetization fault. In the first step the effects of bearing faults on the BLDC motor speed is studied. Then, using the Discrete Wavelet Transform (DWT) and the Continuous Wavelet Transform (CWT), a residual signal is extracted from the speed signal and analyzed in the time-frequency domain. It will be shown that the residual signal contains the main features of bearing faults such as bearing fault frequencies. In the second step the effects of the demagnetization fault on the hall sensors outputs are studied. It is shown that the demagnetization fault leads to impulsive oscillations in the duty cycle signal of the hall sensors outputs. Using the kurtosis index of the duty cycle signal, the rotor condition is classified into either a healthy condition or a demagnetized condition. In the third step, using an experimental setup, the efficiency of the proposed methods is demonstrated. The proposed algorithms are simple and only use hall sensors for fault detection, and hence lead to a simple and cheaper fault diagnosis system.

Time-series classification is utilized in a variety of applications leading to the development of many data mining techniques for time-series analysis. Among the broad range of time-series classification algorithms, recent studies are considering the impact of deep learning methods on time-series classification tasks. The quantity of related publications requires a bibliometric study to explore most prominent keywords, countries, sources and research clusters. The paper conducts a bibliometric analysis on related publications in time-series classification, adopted from Scopus database between 2010 and 2019. Through keywords co-occurrence analysis, a visual network structure of top keywords in time-series classification research has been produced and deep learning has been introduced as the most common topic by additional inquiry of the bibliography. The paper continues by exploring the publication trends of recent deep learning approaches for time-series classification. The annual number of publications, the productive and collaborative countries, the growth rate of sources, the most occurred keywords and the research collaborations are revealed from the bibliometric analysis within the study period. The research field has been broken down into three main categories as different frameworks of deep neural networks, different applications in remote sensing and also in signal processing for time-series classification tasks. The qualitative analysis highlights the categories of top citation rate papers by describing them in details.

In this paper, a robust sensor fault detection and isolation (FDI) method based on the unknown input observer (UIO) approach is presented. The basic principle of unknown input observers is to decouple disturbances from the state estimation error. A single full-order observer is designed to detect sensor faults in the presence of unknown inputs (disturbances). By doing so, we generate a residual, a weighted output of the state estimation error, decoupled from disturbances. The resulting robust (in the sense of disturbances) residual can be used for fault detection. Although this scheme has successful fault detection, using one observer is not successful in fault isolation. Therefore, a robust sensor fault isolation observer scheme is proposed. In order to evaluate its ability, the presented method is adopted to detect and isolate sensor faults of a highly nonlinear dynamic system. The faulty behavior of output sensors in a jacketed continuous stirred tank reactor (CSTR), around operating point, is investigated. Simulation results show that model uncertainties and disturbances can be distinguished from a response to a sensor fault.

In this paper, neural network‐based data fusion is used to detect fault and isolate stator winding short circuit, outer bearing race, and broken rotor bar defects in an induction motor. In addition, the robustness of the proposed method against the disturbance introduced by the coupled pump's unbalanced power source and dry running is investigated. First, three‐phase current and voltage signals are separated by means of independent component analysis (ICA), then extracted features are combined by adopting neural networks, and finally, the system's health condition is evaluated. Experimental results indicate that data fusion based on neural networks can evaluate with high reliability the system's health condition and provide better robustness in the presence of disturbances.