This research presents a novel Customised Load Adaptive Framework (CLAF) for fault classification in Induction Motors (IMs), utilising the Machinery Fault Prevention Technology (MFPT) bearing dataset. CLAF represents a pioneering approach that extends traditional fault classification methodologies by accounting for load variations and dataset customisation. Through a meticulous two-phase process, it unveils load-dependent fault subclasses that have not been readily identified in traditional approaches. Additionally, new classes are created to accommodate the dataset’s unique characteristics. Phase 1 involves exploring load-dependent patterns in time and frequency domain features using one-way Analysis of Variance (ANOVA) ranking and validation via bagged tree classifiers. In Phase 2, CLAF is applied to identify mild, moderate, and severe load-dependent fault subclasses through optimal Continuous Wavelet Transform (CWT) selection through Wavelet Singular Entropy (WSE) and CWT energy analysis. The results are compelling, with a 96.3% classification accuracy achieved when employing a Wide Neural Network to classify proposed load-dependent fault subclasses. This underscores the practical value of CLAF in enhancing fault diagnosis in IMs and its future potential in advancing IM condition monitoring. Full article

This paper analyzes vibration data that shows sudden amplitude changes due to non-stationary load conditions. The data were recorded in a wind turbine that operated under gusty winds and showed high peaks during short periods. Data were analyzed with the auto-regressive integrated moving average (ARIMA) algorithm, and the results were compared to the exponential forecasting method. Other methods have been applied for forecasting vibration data, but the simplicity of this method makes it suitable for rotating machinery with high variable loading conditions. The analysis of the method’s parameters is included in this paper, and the results showed that the optimum configuration depends on the data variations and the existence of significant trends. Forecasting vibration data is challenging; it depends on the source data quality, the preprocessing algorithms, and the deterioration of the mechanical elements. Predictions become less accurate when the machine operates under sudden changes, and evaluating damaging effects caused by the sudden event is difficult to estimate. Full article

Infrared thermography (IRT) has become an interesting alternative for performing condition assessments of different types of induction motor (IM)-based equipment when it operates under harsh conditions. The reported results from state-of-the-art articles that have analyzed thermal images do not consider (1): the presence of more than one fault, and (2) the inevitable noise-corruption the images suffer. Bearing in mind these reasons, this paper presents a convolutional neural network (CNN)-based methodology that is specifically designed to deal with noise-corrupted images for detecting the failures that have the highest incidence rate: bearing and broken bar failures; moreover, rotor misalignment failure is also considered, as it can cause a further increase in electricity consumption. The presented results show that the proposal is effective in detecting healthy and failure states, as well as identifying the failure nature, as a 95% accuracy is achieved. These results allow considering the proposal as an interesting alternative for using IRT images obtained in hostile environments. Full article

The new technological developments have allowed the evolution of the industrial process to this new concept called Industry 4.0, which integrates power machines, robotics, smart sensors, communication systems, and the Internet of Things to have more reliable automation systems. However, electrical rotating machines like the Induction Motor (IM) are still widely used in several industrial applications because of their robust elements, high efficiency, and versatility in industrial applications. Nevertheless, the occurrence of faults in IMs is inherent to their operating conditions; hence, Inter-turn short-circuit (ITSC) is one of the most common failures that affect IMs, and its appearance is due to electrical stresses leading to the degradation of the stator winding insulation. In this regard, this work proposes a diagnosis methodology capable of performing the assessment and automatic detection of incipient electric faults like ITSC in IMs; the proposed method is supported through the processing of different physical magnitudes such as vibration, stator currents and magnetic stray-flux and their fusion of information. Certainly, the novelty and contribution include the characterization of different physical magnitudes by estimating a set of statistical time domain features, as well as their fusion following a feature-level fusion approach and their reduction through the Linear discriminant Analysis technique. Furthermore, the fusion and reduction of information from different physical magnitudes lead to performing automatic fault detection and identification by a simple Neural-Network (NN) structure since all considered conditions can be represented in a 2D plane. The proposed method is evaluated under a complete set of experimental data, and the obtained results demonstrate that the fusion of information from different sources (physical magnitudes) can lead to achieving a global classification ratio of up to 99.4% during the detection of ITSC in IMs and an improvement higher than 30% in comparison with classical approaches that consider the analysis of a unique physical magnitude. Additionally, the results make this proposal feasible to be incorporated as a part of condition-based maintenance programs in the industry. Full article

Induction motors are indispensable, robust, and reliable machines for industry; however, as with any machine, they are susceptible to diverse faults. Among the faults that a motor can suffer, broken rotor bars (BRBs) have become one of the most studied ones because the motor under this fault condition can continue operating with apparent normality, yet the fault severity can quickly increase and, consequently, generate the whole collapse of the motor, raising repair costs and the risk to people or other machines around it. This work proposes an expert system to detect BRB early, i.e., half-BRB, 1-BRB, and 2-BRB, from the current signal analysis by considering the following two operating regimes: start-up transient and steady-state. The method can diagnose the BRB condition by using either one regime or both regimes, where the objective is to somehow increase the reliability of the result. Regarding the proposed expert system, it consists of the application of two autoencoders, i.e., one per regime, to diagnose the BRB condition. To automatically separate the regimes of analysis and obtain the envelope of the current signal, the Hilbert transform is applied. Then, the particle swarm optimization method is implemented to compute the separation point of both regimes in the current signal. Once the signal is separated, the two autoencoders and a simple set of if-else rules are employed to automatically determine the BRB condition. The proposed expert system proved to be an effective tool, with 100% accuracy in diagnosing all BRB conditions. Full article