The electrical endurance of an electromagnetic alternating current (ac) contactor can be reflected by the mass loss of its contacts during operations. This paper carries out ac contactor endurance tests in which arcing phase angles (APAs) are fixed. Contact mass losses are measured, and the electrical signals of the contactor circuit are acquired. Thus, the corresponding energies of each break process are computed. This paper investigates the relation of arcing phases, arcing energies, and mass losses of break processes, and concludes that the higher arcing energies cause greater contact mass losses. Then, this paper proposes a breaking strategy that controls the APAs to minimize and balance the contact mass losses of the three phases of an ac contactor. Therefore, the erosion on each contact is reduced and the total operation number can be increased. Compared with random breaking, the novel strategy significantly improves the electrical lifetime of the ac contactors.

The paper proposes a deep-learning based approach that predicts electrical endurance of alternating current (AC) contactors by using measured electric waveforms. We execute AC-4 tests and acquire voltage and current waveforms during breaking processes. For every waveform, we extract signals within a period in which the breaking arc occurs. Extracted three-phase signals of every breaking operation are reformed to a 32×32×6 example. We also measure the accumulated mass loss of the contacts to represent the electrical endurance condition and label the examples according to the mass loss proportion. Convolutional neural networks (CNN) are employed to build prediction models. Both a LeNet5-like CNN structure (CNN-A) and an improved CNN structure (CNN-B) with 3D convolution layers are implemented to train classification and regression models. Experimental results show that CNN-B for classification achieves the best prediction accuracy. Then a voting mechanism is utilized for accuracy improvement and achieves the prediction accuracy of 82.14% for 10 classes and 90.98% for 5 classes. Especially, the accuracy is over 90% for the last classes which represent the ending condition of electrical endurance. Therefore, it is proven that the method can be utilized to predict electrical endurance for the AC-4 condition.

Arc faults are the main cause of electrical fires according to national fire data statistics. Intensive studies of artificial intelligence-based arc fault detection methods have been carried out and achieved a high detection accuracy. However, the computational complexity of the artificial intelligence-based methods hinders their application for arc fault detection devices. This paper proposes a lightweight arc fault detection method based on the discrimination of a novel feature for lower current distortion conditions and the Adam-optimized BP neural network for higher distortion conditions. The novel feature is the pulse signal number per unit cycle, reflecting the zero-off phenomena of the arc current. Six features, containing the novel feature, are chosen as the inputs of the neural network, reducing the computational complexity. The model achieves a high detection accuracy of 99.27% under various load types recommended by the IEC 62606 standard. Finally, the proposed lightweight method is implemented on hardware based on the STM32 series microcontroller unit. The experimental results show that the average detection accuracy is 98.33%, while the average detection time is 45 ms and the average tripping time is 72–201 ms under six types of loads, which can fulfill the requirements of real-time detection for commercial arc fault detection devices.

This article proposes a method that indirectly measures the contact erosion of alternating current (ac) contactors via mapping electrical signals to the contacting alloy erosion condition which is represented by the accumulated contact mass loss (ACML). Electrical signal waveforms and their corresponding ACMLs of every make-and-break operation are acquired in endurance tests. A supervised convolutional neural network regression (CNNR) architecture containing six 1-D convolution layers is proposed to model the relation between waveforms and ACMLs. We compare different CNNR architectures as well as different training schemes by the test precision to obtain the optimal solution. Experiments prove that the proposed CNNR architecture with an optimized training scheme can achieve a precise ACML measurement when only voltage waveforms of make operations are used. The best results reach mean absolute errors of 3.29% and 1.59% corresponding to two datasets, respectively, which are superior to the results of other regression methods in the comparison and prove the theoretical significance and application values.

This paper proposes a metallic fracture estimation method that combines digital image correlation and convolutional neural networks, based on a proven theory that the strain distribution of a component changes when a crack occurs in a structure. By using digital image correlation, the method achieves noncontact and nondestructive sensing, as well as high interference immunity. We utilize a digital image correlation system to produce strain distribution graphs that reflect occurrences and propagations of fractures during fatigue processes. A deep residual network (ResNet) regression model is trained by correlating strain distribution graphs with the corresponding fracture lengths, so that the fracture propagation condition can be estimated by data from digital image correlation. In the experiment, according to the American Society for Testing Materials (ASTM) standards, we fabricate a set of aluminum specimens and perform fatigue tests with data acquisition by digital image correlation. Finally, we obtain a crack length estimation mean absolute error of 0.0077 mm, or 0.26% of the measuring range. The results show the precision, as well as the practicality, of the proposed method.

This paper is dedicated to achieving flexible automatic assembly of miniature circuit breakers (MCBs) to resolve the high rigidity issue of existing MCB assembly by proposing a flexible automatic assembly process and method with industrial robots. To optimize the working performance of the robot, a time-optimal trajectory planning method of the improved Particle Swarm Optimization (PSO) with a multi-optimization mechanism is proposed. The solution uses a fitness switch function for particle sifting to improve the stability of the acceleration and jerk of the robot motion as well as to increase the computational efficiency. The experimental results show that the proposed method achieves flexible assembly for multi-type MCB parts of varying postures. Compared with other optimization algorithms, the proposed improved PSO is significantly superior in both computational efficiency and optimization accuracy. Compared with the standard PSO, the proposed trajectory planning method shortens the assembly time by 6.9 s and raises the assembly efficiency by 16.7%. The improved PSO is implemented on the experimental assembly platform and achieves smooth and stable operations, which proves the high significance and practicality for MCB fabrication.