The most common cause of electric motor failure is the bearings, and so methods for fast and accurate diagnosis of motor bearing failure are urgently needed. Traditional fault diagnosis methods have high uncertainty and complexity since they require manual extraction of features. Deep learning has shown good performance in electrical equipment fault detection, and it can directly complete end-to-end diagnosis of motor faults, avoiding human involvement. Here, a new fault diagnosis method is presented which combines Gramian angular field (GAF) image coding, extreme learning machine (ELM) and convolutional neural network (CNN). The method has three main stages: First of all, GAF is utilized to convert the acquired vibration break signals into 2-D pictures. Next, the enhanced CNN model is taken to identify the elements of the converted image quickly and accurately. Finally, the ELM is used as the final classifier to gain further accuracy and diagnostic speed of fault classification. Experiments were designed to validate the proposed method using two different motor bearing fault datasets at Case Western Reserve University and autonomous experiment and performance is compared with several commonly used intelligent diagnosis algorithms. The proposed method’s accuracy in the experiment designed in this paper can reach 99.2% at most, and it only takes 0.835s to complete the diagnosis, which outperforms traditional diagnostic methods on both datasets and improving the maximum diagnostic accuracy by 33.6%. The findings indicate that this method can classify various fault types efficaciously, and has the benefits of quick diagnosis, high accuracy, and good generalization ability.

To precisely and timely control the shaft speed for a marine electric propulsion system under normal sea conditions, a new shaft speed control technique combining the evidential reasoning rule with the traditional PID controller was proposed in this study. First, an intelligent adaptive PID controller based on the evidential reasoning rule was designed for a marine electric propulsion system to obtain the PID parameters K_P, K_I, and K_D. Then, a local iterative optimization strategy for model parameters was proposed. Furthermore, the parameters of the adaptive PID controller model were optimized in real time by using the sequential linear programming algorithm, which enabled the adaptive adjustment of K_P, K_I, and K_D. Finally, the performance of the adaptive PID controller regarding the shaft speed control was compared with that of other controllers. The results showed that the adaptive PID controller designed in this study had better control performance, and the shaft speed control method based on the adaptive PID controller could better control the shaft speed of the marine electric propulsion system.

Motor vibration signal data sets are characteristically random and nonlinear, and its features are difficult to extract for fault identification. To reduce the uncertainty of fault diagnosis, a method based on principal component analysis (PCA) and discrete belief rule base (DBRB) was developed for the first time. Initially, the vibration signal was first denoised using a wavelet threshold algorithm to eliminate interference. Second, overlapping signals were segmented into 15 time windows and a total of 13 typical time domain features and mathematical statistical features were extracted. Third, the dimensions of the features were reduced to three principal components by PCA and were taken as the antecedent attributes of the DBRB. However, the amount of information in each principal component is different, so the variance contribution rate was taken as an antecedent attribute weight to restore the original data characteristics. Fourth, a PCA-DBRB model was established, which effectively avoided the combinatorial explosion problem of rule base in the DBRB model. In addition, to obtain appropriate reference values, the k-means algorithm was introduced to take the cluster centers as reference values. The method was then validated by collecting typical fault data from motor bench experiments. The results demonstrated that compared with other traditional classifiers, this approach is more effective and superior in classification performance and more accurate in diagnosing faults from motor vibration data.

Capacitance aging of DC link capacitors in voltage source inverters (VSIs) is a common fault which can lead to instability of the DC voltage. In such a failure state, although the VSI can still work, its performance gradually deteriorates, resulting in a shorter service life of the equipment. Here, an online monitoring and fault diagnosis method for capacitance aging based on evidence reasoning (ER) rule is presented. Features from the DC link voltage data with different levels of capacitance aging are extracted, and data features are generated as pieces of diagnostic evidence, which are then combined according to the ER rule. Finally, capacitance aging fault levels were estimated using the combined results. This method has better diagnostic performance compared to a backpropagation (BP) neural network approach and can be used to flexibly define the relative weighting of each evidence parameter depending on the application. This approach can therefore be widely used for fault diagnosis of an array of different devices.