A novel data-driven rollover risk assessment for articulated steering vehicles using RNN

Articulated steering vehicles have outstanding capability operating but suffer from frequent rollover accidents due to their complicated structure. It is necessary to accurately detect their rollover risk for drivers to take action in time. Their variable structure and the variable center of mass exhibit nonlinear time-variant behavior and increase the difficulty of dynamic modelling and lateral stability description. This paper proposes a novel data-driven modelling methodology for lateral stability description of articulated steering vehicles. The running data is first collected based on the typical operations that prone to rollover and then classified into two types: Safety and danger. The data quality is further improved by wavelet transformation. Finally, an RNN model is built on the data. The experimental results show that the output of the RNN model can accurately quantify lateral stability of the vehicle, i.e., the risk of rollover, when it is turning and crossing uneven surfaces or obstacles

Transfer path analysis and its application in low-frequency vibration reduction of steering wheel of a passenger vehicle

Abstract(#br)The demands on improving the noise, vibration and harshness of passenger vehicles are growing rapidly. Low-frequency vibration of steering wheel is one of the most important factors leading to the discomfort of drivers. This study proposes a systematic analysis methodology to reduce the low-frequency vibration of steering wheel using classical transfer path analysis (CTPA). The theoretical basics of TPA using dynamic stiffness approach and inverse matrix approach are briefly introduced, and then the experimental apparatus and analysis procedures in performing the TPA are introduced. The static forces in the rubber mounts of the powertrain system are calculated, the dynamic stiffness of the rubber mounts are estimated, and the operational forces are determined. The contributions of different transfer paths to the vibration of steering wheel are analyzed and compared, and the predominant causes are identified. The results show that the vibration of steering wheel along the X direction is protruded at the engine ignition frequency, and the vibration of the exhaust system along the X direction contributes most to the vibration because of large frequency response function. The mounting structure of the exhaust system is modified based on modal analysis results using finite element method to reduce the vibration of steering wheel

Combined forecasting approach for product quality based on support vector regression and gray forecasting model

Forecasting product quality by incorporating customer satisfaction perception factors is an intriguing research area, which can promote the sustainable development of enterprises. To address small-sample random time series data, this study proposes a combined forecasting approach (CFA) for the product quality index that considers perception factors. The proposed approach is based on the support vector regression (SVR) and an improved gray forecasting model (GFM). First, the study constructs a system of perception factors related to defect parts per million (DPPM). Then, the key perception factors (KPF) are selected using the gray entropy relational degree, which is derived from gray relational analysis and information entropy. Then, a multivariable GFM is proposed based on the weighted Markov and the derived form of the gray model to reduce the forecasting error. Finally, a CFA is constructed considering KPF and optimized based on the SVR and the proposed GFM to forecast the DPPM. A case study of liquid crystal display is conducted to demonstrate the feasibility of the proposed CFA. The forecast error of the proposed CFA is 3.2%, which is better than those of GFM, SVR, and ARIMA (4.01%, 6.21%, and 9.89%, respectively). The comparison and discussion of methods demonstrate the superiority of the proposed approach for forecasting product quality

Energy Flow Analysis of Excavator System Based on Typical Working Condition Load

Accurate energy flow results are the premise of excavator energy-saving control research. Only through an accurate energy flow analysis based on operating data can a practical excavator energy-saving control scheme be proposed. In order to obtain the excavator’s accurate energy flow, the excavator components’ performance and operating data requirements are obtained, and the experimental schemes are designed to collect it under typical working conditions. The typical working condition load is reconstructed based on wavelet decomposition, harmonic function, and theoretical weighting methods. This paper analyzes the excavator system’s energy flow under the typical working condition load. In operation conditions, the output energy of the engine only accounts for 50.21% of the engine’s fuel energy, and the actuation and the swing system account for 9.33% and 4%, respectively. In transportation conditions, the output energy of the engine only accounts for 49.80% of the engine’s fuel energy, and the torque converter efficiency loss and excavator driving energy account for 15.09% and 17.98%, respectively. The research results show that the energy flow analysis method based on typical working condition load can accurately obtain each excavator component’s energy margin, which provides a basis for designing energy-saving schemes and control strategies

Energy Flow Analysis of Excavator System Based on Typical Working Condition Load

Accurate energy flow results are the premise of excavator energy-saving control research. Only through an accurate energy flow analysis based on operating data can a practical excavator energy-saving control scheme be proposed. In order to obtain the excavator’s accurate energy flow, the excavator components’ performance and operating data requirements are obtained, and the experimental schemes are designed to collect it under typical working conditions. The typical working condition load is reconstructed based on wavelet decomposition, harmonic function, and theoretical weighting methods. This paper analyzes the excavator system’s energy flow under the typical working condition load. In operation conditions, the output energy of the engine only accounts for 50.21% of the engine’s fuel energy, and the actuation and the swing system account for 9.33% and 4%, respectively. In transportation conditions, the output energy of the engine only accounts for 49.80% of the engine’s fuel energy, and the torque converter efficiency loss and excavator driving energy account for 15.09% and 17.98%, respectively. The research results show that the energy flow analysis method based on typical working condition load can accurately obtain each excavator component’s energy margin, which provides a basis for designing energy-saving schemes and control strategies

An Integrated Approach for Failure Mode and Effects Analysis Based on Weight of Risk Factors and Fuzzy PROMETHEE Ⅱ

Design experts need to fully understand the failure risk of a product to improve its quality and reliability. However, design experts have different understandings of and concepts in the risk evaluation process, which will lead to cognitive asymmetry in the product’s redesign. This phenomenon of cognitive asymmetry prevents experts from improving the reliability of a product, increasing the risk of product development failure. Traditionally, failure mode and effects analysis (FMEA) has been widely used to identify the failure risk in redesigning products and a system’s process. The risk priority number (RPN), which is determined by the risk factors (RF), namely, the occurrence (O), severity (S), and detection (D), is the index used to determine the priority ranking of the failure modes (FM). However, the uncertainty about the evaluation information for the RF and the coupling relationship within the FM have not been taken into account jointly. This paper presents an integrated approach for FMEA based on an interval-valued intuitionistic fuzzy set (IVIFS), a fuzzy information entropy, a non-linear programming model, and fuzzy PROMETHEE Ⅱ to solve the problem of cognitive asymmetry between experts in the risk evaluation process. The conclusions are as follows: Firstly, an IVIFS is used to present the experts’ evaluation information of the RF with uncertainty, and the fuzzy information entropy is utilized to obtain the weight of the experts to integrate the collective decision matrix. Secondly, a simplified non-linear programming model is utilized to obtain the weight of the RF to derive the weighted preference index of the FM. Subsequently, the coupling relationship within the FM is estimated by fuzzy PROMETHEE Ⅱ, where the net flow is given to estimate the priority ranking of the FM. Finally, the proposed approach is elaborated on using a real-world case of a liquid crystal display. Methods comparison and sensitivity analyses are conducted to demonstrate the validity and feasibility of the proposed approach

An Integrated Approach for Failure Mode and Effects Analysis Based on Weight of Risk Factors and Fuzzy PROMETHEE Ⅱ

Design experts need to fully understand the failure risk of a product to improve its quality and reliability. However, design experts have different understandings of and concepts in the risk evaluation process, which will lead to cognitive asymmetry in the product’s redesign. This phenomenon of cognitive asymmetry prevents experts from improving the reliability of a product, increasing the risk of product development failure. Traditionally, failure mode and effects analysis (FMEA) has been widely used to identify the failure risk in redesigning products and a system’s process. The risk priority number (RPN), which is determined by the risk factors (RF), namely, the occurrence (O), severity (S), and detection (D), is the index used to determine the priority ranking of the failure modes (FM). However, the uncertainty about the evaluation information for the RF and the coupling relationship within the FM have not been taken into account jointly. This paper presents an integrated approach for FMEA based on an interval-valued intuitionistic fuzzy set (IVIFS), a fuzzy information entropy, a non-linear programming model, and fuzzy PROMETHEE Ⅱ to solve the problem of cognitive asymmetry between experts in the risk evaluation process. The conclusions are as follows: Firstly, an IVIFS is used to present the experts’ evaluation information of the RF with uncertainty, and the fuzzy information entropy is utilized to obtain the weight of the experts to integrate the collective decision matrix. Secondly, a simplified non-linear programming model is utilized to obtain the weight of the RF to derive the weighted preference index of the FM. Subsequently, the coupling relationship within the FM is estimated by fuzzy PROMETHEE Ⅱ, where the net flow is given to estimate the priority ranking of the FM. Finally, the proposed approach is elaborated on using a real-world case of a liquid crystal display. Methods comparison and sensitivity analyses are conducted to demonstrate the validity and feasibility of the proposed approach