Multistage degradation modeling for BLDC motor based on Wiener process

High speed permanent magnet (PM) brushless dc (BLDC) motors have increasingly been used in a wide range of commercial and industrial applications, while low rotor loss and reliable rotor dynamic feature of the motor are essential requirements. Due to the high power densities, the high speed PM BLDC motors' unit volume losses are high, and also it is difficult to disperse heat, thus, the rotor losses are no longer negligible compared to the conventional-speed machines and must be considered in the design process. Besides, the rotors of high speed PM BLDC motors are subject to the resonance vibration, limiting the maximum operation speed. This paper describes the design and analysis of a high speed PM BLDC motor with surface mounted magnets for a rated power of 10kW at 70,000 rpm. Influences of the stator and rotor structures as well as magnet materials on the motor's electromagnetic performance and rotor dynamic property are analyzed. Both analytical and FEA methods are adopted to calculate the motor losses and rotor resonance modals with different motor configurations.

Nowadays, sensor technology is used in a wide range of fields, and the demand for its reliability continues to increase. Accurate estimation of the remaining useful life of sensors is critical. Since the performance degradation of sensors involves multiple stages, the single-stage Wiener degradation model is difficult to accurately characterize. To resolve this issue, a multi-stage Wiener process degradation model is established. The model utilizes the cumulative sum algorithm to identify change points among stages. Then, the performance of the multi-stage Wiener model for characterizing degradation trends is validated with load cell accelerated aging test data. Furthermore, the results of experiment demonstrate that the proposed method provides a more accurate remaining useful life estimation compared to the single-stage Wiener model, and the relative error is reduced by 18.82%.

Electronic packaging solder joints are the key parts of mechanical fixation and electrical interconnection between electronic chips and printed circuit boards, which are prone to failure and lead to electronic device failures under the action of vibration environment stress. In regard to the failure characterization and degradation modeling of electronic packaging solder joints under vibration load, this paper adopts environmental stress tests, builds a vibration failure test platform, designs a vibration load excitation spectrum, and obtains solder joint degradation data under vibration stress; it uses the square root amplitude, form factors, and kurtosis factors to characterize the solder joint degradation process, which effectively identify the solder joint degradation node, and improve the data monotonicity of the solder joint degradation process; the Wiener process is used to build a solder joint multi-stage degradation model. Based on the EM algorithm, the hyperparameters of the degradation model have been optimized, and the universal test of the Wiener degradation model with multiple samples is carried out in accordance with the LB index. The analysis shows that the effectiveness of different samples is as high as 87.5%, which verifies the universality of the Wiener degradation model; Based on the crack morphology subject to the solder joint test and the features of the solder joint in the multi-stages, the paper analyzes the crack propagation behavior of the solder joint, and clarifies the failure mechanism of the solder joint.

A blind identification method for non-linear Wiener models is investigated. When the input signal of the system does not adopt a Gaussian random signal, the identification process with the input signal is changed into the one without input signal by using the first-order statistical properties of the cyclostationary input signal and the inverse mapping of the non-linear part of the model initially, moreover, all internal variables are recovered only based on the output signal. Then, the estimates of the order and parameters of the model are obtained by using the support vector machine regression theory and the higher-order cumulants principle. Finally, compared with other methods, the simulation results show the effectiveness of the proposed method for identifying non-linear Wiener models.

As the core component responsible for high-frequency power switching in photovoltaic inverters, accurately predicting the remaining useful life (RUL) of insulated gate bipolar transistors (IGBTs) has become a key factor in ensuring the stable operation of photovoltaic systems. However, existing methods struggle to precisely characterize the degradation characteristics and processes of IGBTs at different time points. To address these issues, this paper proposes a MIG-PI-Pathformer RUL prediction method that integrates physical degradation models with deep learning. This method establishes a multi-stage Inverse Gaussian degradation model based on the physical failure mechanisms of IGBTs and couples it with the dual attention mechanism of the Pathformer model to capture complex degradation features, adaptively divide time scales, and thereby correct prediction errors in the physical model; Additionally, physical rule constraints are incorporated into the Pathformer loss function to ensure that RUL predictions align with degradation mechanisms. Simulation results show that, on NASA’s IGBT aging dataset, compared to the single Pathformer, the proposed method reduces MSE and MAE by 70.21% and 17.84%, respectively, and improves R2 by 7.66%. This method provides more accurate and physically interpretable technical support for fault warning and optimized maintenance of photovoltaic inverters.

To precisely control the speed of BLDC motors at high speed and with very good performance, an accurate motor model is required. As a result, the controller design can play an important role in the effectiveness of the system. The classic controllers such as PID are widely used in the BLDC motor controllers, but they are not appropriate due to non-linear model of the BLDC motor. To enhance the performance and speed of response, many studies were taken to improve the adjusting methods of PID controller gains by using fuzzy logic. Use of fuzzy logic considering approximately interpretation of the observations and determination of the approximate commands, provides a good platform for designing intelligent robust controller. Nowadays type-2 fuzzy logic is used because of more ability to model and reduce uncertainty effects in rule-based fuzzy systems. In this paper, an interval type-2 fuzzy logic-based proportional-integral-derivative controller (IT2FLPIDC) is proposed for speed control of brushless DC (BLDC) motor. The proposed controller performance is compared with the conventional PID and type-1 fuzzy logic-based PID controllers, respectively in MATLAB/Simulink environment. Simulation results show the superior IT2FLPIDC performance than two other ones.

The piezoelectric actuator exhibits both linear dynamics of creep and vibration and nonlinear dynamics of hysteresis. The model identification from experiments can compensate uncertainties in the model and get a satisfactory model for the control. Block-oriented nonlinear model is suitable for modeling this kind of linear and nonlinear hybrid dynamics. In this study, a Wiener model identification method for the piezoelectric actuator is proposed to model the linear and nonlinear dynamics together from the data. The model is called two-linear(linear+linear)-nonlinear (TLN) Wiener model, where two additive linear dynamic models are used to identify the creep and vibration dynamics, and a nonlinear dynamic model is followed to identify the hysteresis nonlinear features. The TLN-Wiener model is parameterized using Laguerre functions and Prandtl-Ishlinskii (PI) model, and the model identification algorithm is proposed. The experiments on piezoelectric actuators show that the TLN-Wiener model identification method can model the creep, vibration and hysteresis dynamics. The method is applicable to model the rate-dependency dynamics of the piezoelectric actuator.

Accurately assessing reliability and predicting the life of products is an important part of Prediction and Prognostics and Health Management. According to historical degradation data of products, the degradation model of two-stage nonlinear phase-dependent Wiener process is proposed to evaluate the dependability and forecast the lifetime of individual products. Firstly, a two-stage phase-dependent degradation model based on a nonlinear Wiener process was developed to characterize nonlinear nature of the degradation of product performance by simultaneously taking the correlation of degradation stages and the degradation heterogeneity of products into account; Secondly, the model parameters are estimated using EM algorithm based on historical degradation data of products; Finally, simulation studies are used to demonstrate the superiority of the approach for assessing offline reliability, and the validity of the method proposed in this paper is verified through an example of high-voltage pulse capacitors. The results show that compared with the degradation model based on phase-independent nonlinear Wiener processes, the two-stage nonlinear phase-dependent Wiener process degradation model can better capture actual degradation paths of products and provide a more reasonable assessment of lifetime reliability of products.

Recently, the role of a BLDC (Brushless Direct Current) motor is favoring the designers due to its higher efficiency. Most of the applications are already using BLDC motors as a replacement for AC motors and this holds true for innumerous applications, and these motors also reduce the overall system weight. The commutation of BLDC motor is done electronically; hence it is quite simple to control the torque and RPM of the motor even at much higher speed. This paper gives a brief comparative study of three different control schemes i.e Sinusoidal field oriented control (FOC), Field oriented control (FOC), Hysteresis control of a BLDC motor primarily on the aspect of output torque ripple. The study of all the three control schemes is discussed briefly and simulation of control algorithm has been performed.

Reliability assessment has been widely used and studied as one of the important roles in equipment operation and maintenance. Multivariate competing degradation models have been extensively studied, but some non-competing multivariate degradation processes still exist in reality. In this paper, we present a method of reliability assessment based on Wiener process which considering non-competing relationship. The multivariate degradation process studied in this paper consists of two degradation indicators. The Wiener process was applied to model two degradation indicators separately. Bayesian method is used for parameter estimation of unknown parameters of degradation models and quantification of uncertainty. A case study of spool valve is used for the demonstration and validation of the model presented in this paper.

Brushless DC (BLDC) Motor is widely used in home appliance, robot, and vehicle industry due to the merits of high power density, low noise, and simple controllability. A demands for the motor, especially slim-type BLDC motor, is expected to increase continuously since the minimizing trend of the actuators is getting attention from various applications. Because a phase inductance of the slim-type BLDC motor is large, it causes a lagging of current compared with back-electromotive force (EMF) in wide range of speed. As a result, there is a problem that reduces the motor efficiency and increases the phase current. Particularly the problem is more serious in high speed range and heavy load condition. This paper proposed control scheme for high efficiency of slim type BLDC motor which can resolve the problems as mentioned above. To synchronize the phase between the current and back-EMF of BLDC motor, it proposed lead angle control method by using current dynamics characteristic. Also, it represented the accurate calculating method of lead angle by using only the maximum value of phase current and information of DC-link voltage. For the verification of the proposed control scheme, various simulations and experiments are employed. As a result, the efficiency improvement of slim type BLDC motor is confirmed.

Remaining useful life (RUL) prediction is a core technology for equipment operation and health management, and the degradation process of most equipment generally has a long-term correlation. To address this issue, we propose a method to quantify the uncertainty of RUL prediction based on a temporal convolutional network (TCN)-transformer- Wiener Process (WP) degradation model. We give a new generalized RUL prediction framework that employs TCN-Transformer to fit device degradation trends, which can adaptively adjust the fitting of degradation trends. WP is introduced, and the concept of first hitting time (FHT) gives an approximate expression for the probability density function (PDF) of the remaining lifetime of the model, further quantifying the uncertainty of the RUL prediction results. Finally, verification and analysis on the bearing dataset and the tool holder power head degradation dataset, and by comparing with other existing popular methods demonstrated the accuracy and stability of the proposed RUL prediction method. This innovative method provides a robust solution for RUL prediction, offering significant contributions to improving maintenance strategies and operational efficiency.

In this paper, a novel drive method, which is different from the traditional motor drive techniques, for high-speed brushless DC (BLDC) motor is proposed and verified by a series of experiments. It is well known that the BLDC motor can be driven by either pulse-width modulation (PWM) techniques with a constant dc-link voltage or pulse-amplitude modulation (PAM) techniques with an adjustable dc-link voltage. However, to our best knowledge, there is rare study providing a proper drive method for a high-speed BLDC motor with a large power over a wide speed range. Therefore, the detailed theoretical analysis comparison of the PWM control and the PAM control for high-speed BLDC motor is first given. Then, a conclusion that the PAM control is superior to the PWM control at high speed is obtained because of decreasing the commutation delay and high-frequency harmonic wave. Meanwhile, a new high-speed BLDC motor drive method based on the hybrid approach combining PWM and PAM is proposed. Finally, the feasibility and effectiveness of the performance analysis comparison and the new drive method are verified by several experiments.

The brushless DC (BLDC) motor can be controlled either by fixed dc link voltage control with Pulse Width Modulation (PWM) or by variable dc link voltage control with Pulse Amplitude Modulation (PAM). But neither of these can be a proper method for BLDC motor. Since the PWM control induces high current and torque ripple at high speeds and PAM control limits its application at low speeds due to the small DC link voltage generation. So a drive method merging both these methods is planned with Power Factor Correction (PFC). The motor driver consists of a Bridgeless Canonical Switching Cell (BL-CSC) PFC converter and a 3-phase PWM inverter. The BL-CSC PFC converter operates on Discontinuous Continuous Mode (DCM) to achieve unity power factor at the ac mains. The speed of the BLDC motor is controlled either on the converter side by varying the input voltage of the PWM inverter or on the motor side by keeping the inverter input constant. And the bridgeless configuration of the Canonical Switching Cell (CSC) converter provides less power losses due to the partial removal of diode bridge rectifier (DBR). The hybrid drive method or the dual mode control has been analyzed using MATLAB Simulink. The PFC operation is verified and both load & supply variations are also illustrated in this paper.

Aiming at the life prediction problem of high reliability products with random shock response, this paper analyzes the two-dimensional degradation of highly reliable products, the nonlinear Wiener process model was used to analyze the degradation process of the product performance index, and the multivariate nonlinear Wiener process model was established. Based on the sliding window mechanism and the variable step length method, a dynamic variable step size nonlinear Wiener process degradation model was established by dynamically evaluating the parameters of the degradation model. The approximate expression of the probability density function of the residual life was further given. Finally, the validity and practicability of the proposed model and method were verified by an example.