One of the biggest challenges for the fault diagnosis research of industrial robots is that the normal data is far more than the fault data; that is, the data is imbalanced. The traditional diagnosis approaches of industrial robots are more biased toward the majority categories, which makes the diagnosis accuracy of the minority categories decrease. To solve the imbalanced problem, the traditional algorithm is improved by using cost-sensitive learning, single-class learning and other approaches. However, these algorithms also have a series of problems. For instance, it is difficult to estimate the true misclassification cost, overfitting, and long computation time. Therefore, a fault diagnosis approach for industrial robots, based on the Multiclass Mahalanobis-Taguchi system (MMTS), is proposed in this article. It can be classified the categories by measuring the deviation degree from the sample to the reference space, which is more suitable for classifying imbalanced data. The accuracy, G-mean and F-measure are used to verify the effectiveness of the proposed approach on an industrial robot platform. The experimental results show that the proposed approach’s accuracy, F-measure and G-mean improves by an average of 20.74%, 12.85% and 21.68%, compared with the other five traditional approaches when the imbalance ratio is 9. With the increase in the imbalance ratio, the proposed approach has better stability than the traditional algorithms.

Electromagnetic coils are a key component in a variety of systems and are widely used in many industries. Because their insulation usually fails suddenly and can have catastrophic effects, degradation monitoring of coil insulation systems plays a vital role in avoiding unexpected machine shutdown. The existing insulation degradation monitoring methods are based on assessing the change of coil high-frequency electrical parameter response, whereas the effects of the insulation failure mechanisms are not considered, which leads to inconsistency between experimental results. Therefore, this paper investigates degradation monitoring of coil insulation systems under thermal loading conditions from a creep point of view. Inter-turn insulation creep deformation is identified as a quantitative index to manifest insulation degradation changes at the micro level. A method is developed to map coil high-frequency electrical monitoring parameters to inter-turn insulation creep deformation in order to bridge the gap between the micro-level and macro-level changes during the incipient insulation degradation process. Thermally accelerated tests are performed to validate the developed method. The mapping method helps to determine the physical meaning of coil electrical monitoring parameters and presents opportunities for predictive maintenance of machines that incorporate electromagnetic coils.

Electromagnetic coils are one of the key components of many systems. Their insulation failure can have severe effects on the systems in which coils are used. This paper focuses on insulation degradation monitoring and remaining useful life (RUL) prediction of electromagnetic coils. First, insulation degradation characteristics are extracted from coil high-frequency electrical parameters. Second, health indicator is defined based on insulation degradation characteristics to indicate the health degree of coil insulation. Finally, an insulation degradation model is constructed, and coil insulation RUL prediction is performed by particle filtering. Thermal accelerated degradation experiments are performed to validate the RUL prediction performance. The proposed method presents opportunities for predictive maintenance of systems that incorporate coils.

This paper proposes a novel active disturbance rejection prescribed performance controller for a class of strictly feedback nonlinear systems under unknown initial tracking conditions. By introducing a novel algebraic saturation function, the initial value of tracking error is transformed into a bounded range, effectively overcoming the limitation of traditional prescribed performance control that requires prior knowledge of the initial value of tracking error. To address the differential explosion issue arising from the backstepping method, this paper employs dynamic surface processing techniques. The integration of active disturbance rejection control with prescribed performance control significantly enhances the robustness of nonlinear systems. The designed controller ensures that closed-loop systems under unknown initial tracking conditions converge to any small neighborhood near the origin within finite time. The system output satisfies the requirements of the prescribed performance function and exhibits excellent suppression capability against external disturbances.

Electromagnetic coil is the key component of rotating equipment, and its reliability is very important for the normal operation of the equipment. In order to accurately evaluate the health status of electromagnetic coil, a health evaluation and anomaly detection method based on Mahalanobis distance (MD) and Box-Cox transformations is proposed. With the help of accelerated fatigue experiment, the whole life electrical data of high, medium and low frequency spectrum of electromagnetic coil is collected, and the low and medium frequency spectrum data are constructed as the initial feature set. And then, the initial feature set is mahalanized, and Mahalanobis distance is taken as the feature to evaluate the health state of the electromagnetic coil. With the help of Box-Cox transformations, MD is transformed into normal distribution form, and the electromagnetic coil fault early warning is realized according to 3 σ criterion. The results show that MD can accurately evaluate the degradation state of the electromagnetic coil and realize early warning.