Reliability Study of Low-Voltage Electrical Appliances in Transport Vehicles Under Variable-Load Conditions

Induction Machine (IM) fault detection techniques such as Fast Fourier Transform (FFT) which is a popular steady-state analysis method is recognized to be highly dependent on the IM loading and speed conditions. Nonetheless, implementing an FFT or even Short Time Fourier Transform (STFT) will result in low resolution frequency characteristics especially under a variable speed and loading conditions. Consequently, fault detection and classification under variable loading and speed conditions is quite inconvenient. Since, mechanical faults are one of the major breakdowns, which occur in IMs, it needs to be addressed to prevent breakage andfault extension. This paper investigates and detects faults under variable loading and speed conditions by studying the Motor Current Signature Analysis (MCSA) using a novel developed parallel technique based on the discrete wavelet transform (DWT).The proposed model input would be MCSA with the similar drive, loading condition and constraints for both healthy and faulty electric motors and the Discrete Wavelet Transform (DWT) is used as a detection technique. The proposed technique uses the DWT to the IM’s stator current to extract the desired features including Min, Max, Standard deviation, and Energy of the signal as a specific vector for each electric motor. In addition, different mechanical faults including Rotor broken bar(s), eccentricity and bearing have been detected using the proposed technique. Then, the accuracy of proposed parallel model using DWT is verified using experimental set-up of the parallel machines under variable loading and speed conditions for different mechanical faults. Finally the results of the proposed technique for all mechanical faults is tabulated and presented.

Generally, the design of the transmission elements is made by considering the worst weather and load conditions in the power system. When the temperature of the cables and overhead lines is low, these can have a capacity of loading more. This situation causes the lines to design based on static rating calculation to be used inefficiently. Therefore, operational flexibility of power system can be achieved by using dynamic rating methods in overhead lines and underground cables, while the variable weather conditions and loading conditions are considered. Unlike the studies in the literature, the thermal analysis of the cable can be made by considering the changing loading and weather conditions. In the analysis, the wind power plants is discussed for variable loading conditions. As a result of this assumption, it is found that the variable loading has a very important effect on the dynamic line rating, such as weather conditions. The economic consequences of variable load conditions on cable sizing are analyzed using the net present value method by considering the dynamic rating calculations. In the analysis, it is revealed that if the dynamic rating of the cable is used, it will be suitable for short-term investments. In this study, thermal analysis of underground cables is based on numerical method by using Fluent program, while overhead lines are based on analytical method by using the ETAP program.

Permanent magnet synchronous machines have gained popularity in wind turbines due to their merits of high efficiency, power density, and reliability. The wind turbines normally work in a wide range of operations, and harsh environments, so unexpected faults may occur and result in productivity losses. The common faults in the permanent magnet machines occur in the bearing and stator winding, being mainly detected in steady-state operating conditions under constant loads and speeds. However, variable loads and speeds are typical operations in wind turbines and powertrain applications. Therefore, it is important to detect bearing and stator winding faults in variable speed and load conditions. This paper proposes an algorithm to diagnose multiple faults in variable speed and load conditions. The algorithm is based on tracking the frequency orders associated with faults from the normalised order spectrum. The normalised order spectrum is generated by resampling the measured vibration signal via estimated motor speeds. The fault features are then generated from the tracking orders in addition to the estimated torque and speed features. Finally, support vector machine algorithm is used to classify the faults. The proposed method is validated using experimental data, and the validated results confirm its usefulness for practical applications.

In order to predict the most reliable fatigue life under variable loading conditions such as survice environments, a new fatigue test method was developed, and by using it the fatigue crack propagation properties were studied for HT80, SM50B and SUS 304 steels.The test results show that, although no crack closure occurred in both the variable and constant loading tests, the crack propagation rate under the variable loading test condition was much faster than that under the constant loading test condition for all tested alloys especially at low ΔK regions.The maximum acceleration of the crack propagation was dependent of the variable loading amplitude. When the variable load was frequently applied, the crack propagation rate became proportional to second power of ΔK.From these results, it can be said that the new fatigue test method is one of the best methods to predict the most reliable fatigue life under variable loading conditions.

In order to evaluate the vehicle reliability in the process of user's use, a fatigue pseudo damage reliability evaluation model Based on axle head acceleration is proposed. Based on the acceleration, an improved MINER cumulative fatigue damage model is studied. Based on the model, the influence of user condition, running speed, load capacity and pavement condition on vehicle reliability is discussed. The reliability evaluation model of vehicle operation under the condition of user use is established. The model can be used for on-line, short-term and long-term evaluation and prediction of vehicle reliability.

A reliability analysis and evaluation model based on event chain and Bayesian information fusion is proposed for the characteristics of complex space phased‐mission systems (SPMSs), the implementation process of integrated reliability modeling is detailed, and the identification of weak links and reliability evaluation is carried out with the entry, descent, and landing process of Tianwen‐1 Mars Probe as an example. The application results show that the reliability analysis and evaluation model based on event chain and Bayesian information fusion has good engineering applicability, and the method can be extended and applied to the reliability modeling, analysis, and evaluation of complex SPMSs.

Reliable life is defined as the average operation time of power equipment in a given reliability, and it is a significant parameter in reliability evaluation. This paper presents a reliable life calculation method for SF 6 circuit breakers. The reliability evaluation model is established based on Fault Tree Analysis (FTA) method. From FTA, all the bottom events which cause top event (failure of the SF 6 circuit breaker) are found. To estimate the reliability by inspection results and test results of the circuit breakers, Health Index (HI) is presented to calculate the failure probability of each bottom event, then the reliability of the top event is obtained. Collecting the failure probability and the age data of different SF 6 circuit breakers with the same type in power system, the data is applied to estimate the reliability function of two parameters Weibull distribution by the least square method. Finally, the reliable life of the SF 6 circuit breaker in a given reliability is estimated after inversion calculation by reliability function. To make further understanding of the method, an application example is presented, and the calculation result shows the effectiveness and accuracy of the model. According to the detailed analysis, the reliable life has more advantage than the physics life to reflect the reliability level of the power equipment.

Smart Vehicle Control Unit - an integrated BMS and VCU System

Energy management of heavy-duty fuel cell vehicles in real-world driving scenarios: Robust design of strategies to maximize the hydrogen economy and system lifetime

How to accurately evaluate and predict the degradation state of the components with small samples is a critical and practical problem. To address the problems of unknown degradation state of components, difficulty in obtaining relevant environmental data and small sample size in the field of reliability prediction, a reliability evaluation and prediction method based on Cox model and 1D CNN-BiLSTM model is proposed in this paper. Taking the historical fault data of six components of a typical load-haul-dump (LHD) machine as an example, a reliability evaluation method based on Cox model with small sample size is applied by comparing the reliability evaluation models such as logistic regression (LR) model, support vector machine (SVM) model and back propagation neural network (BPNN) model in a comprehensive manner. On this basis, a reliability prediction method based on one-dimensional convolutional neural network-bi-directional long and short-term memory network (1D CNN-BiLSTM) is applied with the objective of minimizing the prediction error. The applicability as well as the effectiveness of the proposed model is verified by comparing typical time series prediction models such as the autoregressive integrated moving average (ARIMA) model and multiple linear regression (MLR). The experimental results show that the proposed model is valuable for the development of reliability plans and for the implementation of reliability maintenance activities.

Grid-connected doubly-fed induction generator (DFIG) based wind energy conversion system (WECS) is widely used in harnessing wind power. The paper attempts to characterize a 15-kW grid-connected DFIG based WECS operating under variable speed and loading conditions. Back to back converter topologies are utilized consisting of rotor side converter (RSC) and grid side converter (GSC) controlled in the synchronous reference frame coordinates. The design, modeling, and control of various system components are deliberated. The interaction of the grid with the proposed DFIG-WECS is analyzed where the regulation of the machine active power is achieved through the control of RSC. The current quality, voltage regulation is achieved by the control of GSC. Case studies involving constant wind speed, variable wind speed, and variable loading are undertaken for the characterization analysis. The performance characterizations of the entire system is validated in Matlab/Simulink simulation environment where the critical performance parameters like DC link voltage, generator torque, DFIG stator current, active and reactive power delivered by the DFIG to the grid, PCC side voltage/ current profile and the PCC current Total harmonic distortion (THD) are rigorously monitored, presented and discussed for each case studies.

The influence of rolling element material on friction torque of angular contact ball bearing under variable speed, variable load, and variable load conditions are studied. Based on the unstable working conditions of variable speed and load, the dynamic model of bearing is established by using CMD language in the ADAMS system, and the simulation analysis is carried out. The results show that under variable speed conditions, the shorter the acceleration time is, the greater the radial and axial loads are, and the greater the friction torque of the bearing is; Under variable load conditions, the friction torque of the bearing increases with the increase of load change frequency, and the friction torque of the hybrid ceramic ball bearing is smaller than that of the GCr15 rolling element bearing. The research results provide a reference for reducing the friction heat generation of bearings and improving the operation reliability of bearings under unstable working conditions.

The objective of this study was to propose a crack growth prediction model for cracked specimens under large and variable amplitude loading conditions. In this study, the concept of change in net-section strain energy was applied to simulate elastic–plastic fracture behavior, and three-dimensional fitting of the results with respect to changes in the net-section strain energy, load ratio, and crack growth rate was performed to create the crack growth prediction model. The validity of the generated model was examined using comparisons with experimental data as well as simulation results from existing crack growth models under several variable loading conditions.

Most of the conventional tracking techniques fail to track maximum power point under partially shaded conditions as the photovoltaic (PV) array characteristic curves exhibit multiple local maxima. This paper put forward an adaptive maximum power point tracking technique (MPPT) for PV array which can perform even in non-uniform irradiance conditions. The proposed method targets on extracting the power of non-shaded PV cells during partial shading condition with the help of forward biased bypass diode across the shaded PV cells. The proposed technique implements Mamdani fuzzy inference system based fuzzy logic to control which is further equipped with DC-DC boost converters. The proposed technique enables the PV array to operate efficiently at variable load and non-uniform irradiance condition. The effectiveness of the proposed MPPT technique is investigated for various partial shaded patterns and variable load conditions and the results are compared with the modified perturb & observe MPPT technique.

During the use of rolling bearings in wind turbines, aero engines, and other equipment, they often operate under variable operating and loading conditions. The practice has shown that the sequence of these loads and their interaction has a significant impact on the fatigue life of rolling bearings. In order to better understand this issue, a modified nonlinear fatigue damage accumulation model, which considers the influences of both load sequence and load interaction, is proposed in this paper and verified using the fatigue testing data for six materials. Then, the verified model was applied to evaluate the fatigue damage accumulation and fatigue life of a rolling bearing. The results have shown that, compared with the existing models, the proposed model can more accurately predict the damage accumulation and fatigue life of the rolling bearing under variable loading conditions.