Real-Time Diagnosis of an In-Wheel Motor of an Electric Vehicle Based on Dynamic Bayesian Networks

Xue, Hongtao; Zhou, Jiawen; Chen, Zhenyu; Li, Zhongxing

doi:10.1109/access.2019.2935770

Cited by 11 publications

(7 citation statements)

References 42 publications

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“…However, a fault condition of in-wheel motors is implicit in the real, especially in the earlier fault period, then it is difficult to observe directly the fault states of in-wheel motors. Usually, the condition signal such as vibration, noise or electric signal is collected to detect the anomalous change in the time, frequency or time-frequency domain, then extract the different SPs for reflecting the fault states of in-wheel motors [37,38]. In that way, SPs in the running process of in-wheel motors can form many observation sequences, and observation sequences in the normal condition are more, but observation sequences in the fault states or transition process from normal state to fault states are lesser even no, especially in construction phase of condition monitoring system.…”

Section: Hmm Based On Wmmmentioning

confidence: 99%

Diagnosis method based on hidden Markov model and Weibull mixture model for mechanical faults of in-wheel motors

Xue¹,

Liu²,

Ding³

et al. 2022

Meas. Sci. Technol.

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To effectively monitor the operation state of in-wheel motor used in electric vehicle and ensure the safety of the whole vehicle, a diagnosis method based on hidden Markov model (HMM) and Weibull mixture model (WMM) is proposed for mechanical fault of in-wheel motor, that is simply known as a WMM-HMM diagnosis method. Firstly, vibration signals of in-wheel motor are extracted for sensitive symptom parameters (SPs) which are used to characterize the operation state and establish the observation sequence. Secondly, Weibull mixture model is employed to expand the limited observation sequence under various operating states of in-wheel motor to obtain sufficient observation sequence as the training sample set of HMM, and HMM parameters are determined through combining supervised learning with unsupervised learning algorithm. Then the WMM-HMM diagnosis models are constructed under low and medium speed conditions respectively. Finally, the corresponding fault in-wheel motors are customized and the test bench is built to verify the proposed method. The test results show that the proposed method can accurately identify the mechanical fault state of in-wheel motor under different conditions and has good generalization and applicability in traditional methods comparison.

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Section: Hmm Based On Wmmmentioning

confidence: 99%

Diagnosis method based on hidden Markov model and Weibull mixture model for mechanical faults of in-wheel motors

Xue¹,

Liu²,

Ding³

et al. 2022

Meas. Sci. Technol.

Self Cite

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“…A PMBLDC motor is installed in the wheel, and the original motor controller is selected. e PMBLDC motor and motor controller are powered by the ZhiDou D1 electric vehicle [25]. e test bench is constructed on the basis of an INSTRON 8800 single-channel electrohydraulic servo test system.…”

Section: Experiments Validationmentioning

confidence: 99%

Dynamic Characteristics Analysis of the Hub Direct Drive‐Air Suspension System from Vertical and Longitudinal Directions

Song

Chen

et al. 2021

Shock and Vibration

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Hub direct-drive electric vehicles are driving with hub motors, which cause negative vibration directly on wheels and suspensions. To better analyze the problems of dynamic deterioration resulted by coupling excitations from random road and unbalanced magnetic pull (UMP), a cooperative hub direct drive-air suspension (HDD-AS) system is developed, and evaluation indices based on vertical and longitudinal dynamic coupling mechanism are proposed. The vertical and longitudinal characteristics in the time domain under different damping coefficients and motor speeds are demonstrated, and comparison between traditional electric wheel and suspension-integrating models and HDD-AS system model is conducted. Then, the responses of random road excitation and UMP are analyzed, and explanations of vertical and longitudinal characteristics in the frequency domain under different damping coefficients are given. These results provide important insights into the influence of coupling excitations and damping coefficient on the HDD-AS system model. Finally, the effectiveness of the proposed model is verified by the experiment on the test bench.

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“…For example, two variables, the relative chirp rate of the linear frequency modulated (LFM) signal and the Doppler focus shift, are used to evaluate the target pathway [19]. The departure angles such as 150, 300, 450, from the dead ahead of the vehicle have been defined for recognizing the target pathway [15], [17], [11], [20], [21]. These methods benefit to improve environment identification for intelligent vehicle.…”

Section: B Recognition Of Target Pathwaymentioning

confidence: 99%

“…Radial basis function neural network has been used to extract the drivable region from the perception grid map, and then guide autonomous running in unstructured environments [13], [14]. Fuzzy neural network (FNN) [15], deep reinforcement learning [16], dynamic Bayesian network [17] and high precision GPS positioning [18] is applied to formulate transfer trajectory and establish a management system for intelligent land-vehicle model.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Controller for Autonomous Vehicle Based on Rough Sets

Xue

Zhang

et al. 2019

IEEE Access

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This article presents a fuzzy controller for autonomous vehicle to intelligently recognize running environment and avoid an obstacle, which is constructed by rough sets (RSs) and an adaptive neurofuzzy inference system (ANFIS). Firstly, RSs are considered to propose a pyramid normalization (PN) method for normalizing state parameters (SPs) which are defined to recognize relative position such as distance and angle among a vehicle, an obstacle and target pathway, to improve the adaptability of complex environment and optimize the database of driving knowledge. Secondly, ANFIS is employed to design a controller with self-position azimuth correction (SPAC) for performing trajectory tracking and obstacle avoidance. Finally, the proposed methods have been implemented on the model vehicle called ''RoboCar'', and compared with various fuzzy control approaches such as the initial SPs with ANFIS, the normalized SPs with fuzzy neural network (FNN), and the initial SPs with FNN. Time, maximum tracking error and mean tracking error are calculated to evaluate the performance. The experimental results with four kinds of target pathways have shown that the PN-ANFIS-based controller has saved time (7.6%), reduced maximum tracking error (8.1%) and mean tracking error (8.5%).

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Real-Time Diagnosis of an In-Wheel Motor of an Electric Vehicle Based on Dynamic Bayesian Networks

Cited by 11 publications

References 42 publications

Diagnosis method based on hidden Markov model and Weibull mixture model for mechanical faults of in-wheel motors

Diagnosis method based on hidden Markov model and Weibull mixture model for mechanical faults of in-wheel motors

Dynamic Characteristics Analysis of the Hub Direct Drive‐Air Suspension System from Vertical and Longitudinal Directions

Fuzzy Controller for Autonomous Vehicle Based on Rough Sets

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