Telematics box (T-Box) chip-level Global Navigation Satellite System (GNSS) receiver modules usually suffer from GNSS information failure or noise in urban environments. In order to resolve this issue, this paper presents a real-time positioning method for Extended Kalman Filter (EKF) and Back Propagation Neural Network (BPNN) algorithms based on Antilock Brake System (ABS) sensor and GNSS information. Experiments were performed using an assembly in the vehicle with a T-Box. The T-Box firstly use automotive kinematical Pre-EKF to fuse the four wheel speed, yaw rate and steering wheel angle data from the ABS sensor to obtain a more accurate vehicle speed and heading angle velocity. In order to reduce the noise of the GNSS information, After-EKF fusion vehicle speed, heading angle velocity and GNSS data were used and low-noise positioning data were obtained. The heading angle speed error is extracted as target and part of low-noise positioning data were used as input for training a BPNN model. When the positioning is invalid, the well-trained BPNN corrected heading angle velocity output and vehicle speed add the synthesized relative displacement to the previous absolute position to realize a new position. With the data of high-precision real-time kinematic differential positioning equipment as the reference, the use of the dual EKF can reduce the noise range of GNSS information and concentrate good-positioning signals of the road within 5 m (i.e. the positioning status is valid). When the GNSS information was shielded (making the positioning status invalid), and the previous data was regarded as a training sample, it is found that the vehicle achieved 15 minutes position without GNSS information on the recycling line. The results indicated this new position method can reduce the vehicle positioning noise when GNSS information is valid and determine the position during long periods of invalid GNSS information.

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This study aimed at addressing the difficulties entailed in accurately determining the working loads of screwed joints (SJs) by establishing mechanical models and verifying the accuracy of the numerical calculation model of antiloosening performance under complex working conditions. First, considering the slip state of the interface and the stress state of the thread surface, a corresponding mechanical model was established to investigate the quantitative model of the interaction amongst structural parameters, complex working loads, and antiloosening performance of SJs. The applicability of existing models is expanded by this new model. Second, a load calibration test, an actual working condition test, and a dynamic simulation were combined to accurately determine the load under complex working conditions. A new experimental scheme for measuring the critical residual preload was employed to verify the reliability and accuracy of the numerical calculation model. The results confirmed that structural safety is ensured and that accident risk is reduced. Finally, based on this model, the transverse load, axial load, bending moment, torque about the bolt axis, clamping eccentricity, loading eccentricity, and coefficient of friction in the thread and at the interface were analyzed in terms of the antiloosening performance. The results of this study are expected to provide significant guidance to engineering practices. Moreover, the numerical calculation model can accurately predict the antiloosening performance and failure and also provide technical support for improving the structural reliability, particularly for key screwed-joint structures (SJSs), under complex working conditions loading.