Qinglin Xie

• PhD student
• Nobody at Southwest Jiaotong University

Identifying abnormal data to improve data quality is of great importance for machinery health monitoring (MHM). Existing abnormal data detection methods generally depend on appropriate parameter settings and prior knowledge of data distribution, which result in relatively low adaptability to MHM data. To obtain more reliable MHM results, this paper...

Rail corrugation is a very common wear phenomenon occurring on rail surface, especially on sharp curves, and is one of the main excitation sources of noise and vibration during railway transportation. Timely monitoring of rail corrugation is of great benefit to make scheduled maintenance and save maintenance costs. This paper proposes a novel me...

Accurate monitoring of track irregularities is very helpful to improving the vehicle operation quality and to formulating appropriate track maintenance strategies. Existing methods have the problem that they rely on complex signal processing algorithms and lack multi-source data analysis. Driven by multi-source measurement data, including the axle...

Wheel polygonal wear, especially high-order polygonal wear, has emerged as a significant challenge for Chinese railways over the past decade. The wheels of high-speed trains, locomotives and metro trains suffered from polygonal wear. Wheel out-of-roughness (OOR) of more than 43,000 wheels was collected in this study, including 30,500 high-speed tra...

The wheelset lateral force is an important indicator to evaluate the safety of the metro and it is significantly important to monitor the state online. An indirect measurement model of wheelset lateral force is developed by Kalman filter & Long short-term memory (KF-LSTM) algorithm. First, the process and observation equation of the Kalman filter a...

Wheel polygons can significantly impair the operational quality, service life and ride comfort of railway systems and even threaten the safety of train operations. Timely detection of wheel polygons is of great benefit in formulating a reasonable wheel maintenance strategy. First, to investigate the vibration response of the locomotive under wheel...

Wear prediction for train wheels is essential for evaluating the health status of wheel-rail systems. Existing prediction approaches mainly focus on the physics-based approach or data-driven approach, which either involve complex mechanisms or lack interpretability. A data-driven wear prediction method regarding domain knowledge and multisource sig...

To address the shortcomings of high-cost and poor-timeliness of track irregularity detection, an intelligent track irregularity identification method with multi-source vehicle data drive based on the correlation between vehicle dynamic responses and track irregularities was proposed. Firstly, a subway vehicle system dynamic model was established to...

Recovering missing data to improve data quality is of great importance for prognostics and health management (PHM) of structures and machinery. Existing missing data reconstruction methods are generally limited by strong assumptions such as signal sparsity, coupled with heavy computational burdens, making them poorly adaptable to PHM data. To obtai...

The invention relates to the technical field of wheel polygon detection, in particular to a high-speed train wheel polygon quantitative detection method driven by an improved frequency domain integration method, which comprises the following steps of: 1) Acquiring axle box acceleration ABA and speed signals from a running train, and dividing data i...

Timely monitoring of wheel polygon is of great importance for the formulation of railway wheel maintenance strategies. In this study, a novel data-driven method for onboard and quantitative detection of wheel polygon is presented. First, the axle box acceleration (ABA) signal preprocessing method and stationarity test are introduced to select the r...

The invention relates to the technical field of rail corrugation detection, in particular to a convolution regression-based vehicle-mounted quantitative detection method for rail corrugation roughness, which comprises the following steps of: 1. preprocessing and standardizing data, namely uniformly standardizing an axle box acceleration ABA signal...

The invention relates to the technical field of mechanical structure health monitoring, in particular to a self-adaptive SHM data cleaning method based on local outlier factors, which comprises the following steps: 1. dividing SHM data of the mechanical structure to be analyzed into a series of segments by using an adaptive sliding window ASW techn...

A novel algorithm for quantitative detection of rail corrugation roughness levels based on the axle box acceleration (ABA) signal is proposed. A convolutional regression model, named RCNet, was designed to extract the ABA features and detect the wavelengths and roughness levels of rail corrugation quantitatively. Dynamics simulations and field expe...

The invention relates to monitoring of rail transit wheel-track short wave irregularity, in particular to a convolutional neural network construction method for rail track corrugation identification, which comprises the method for collecting and processing specific vibration information of a rail transit carrier, frequency domain data in a frequenc...

The invention relates to monitoring of short-wave irregularity of a rail transit wheel track, in particular to a rail corrugation identification method, which comprises the steps of acquiring specific vibration information generated when a vehicle moves along a rail in real time through a vibration sensor arranged on the vehicle to obtain vibration...

The application provides a training method, a using method, computer equipment and a computer readable storage medium for intelligently identifying an AI model in a wheel out-of-round state, which are beneficial to improving the accuracy of prediction of the AI model aiming at vibration response. The training method comprises the following steps: o...

The application provides a training method, a using method, a computer device and a computer readable storage medium of a regression prediction AI model of a polygonal wear waveform of a wheel. The training method comprises the following steps: obtaining vibration detection data of a detected wheel in the running process; extracting data from the v...

Rail corrugation is a very common wear phenomenon occurring on rail surface, especially on sharp curves, and is one of the main excitation sources of noise and vibration during railway transportation. Timely monitoring of rail corrugation is of great benefit to make scheduled maintenance and save maintenance costs. This paper proposes a novel metho...

High-order and low-order wheel out-of-roundness (OOR) often occur on metro train wheels, which can intensify the wheel–rail dynamic interaction. A vehicle–track rigid-flexible coupled dynamics model is built through combining the dynamics software SIMPACK with the finite element software ANSYS, which is validated by field vibration measurement resu...

A denosing method that combines morphology filtering with moving average smoothing was proposed to overcome the shortcoming of unsmoothness in morphology filter. In order to provide a reference for the selection of structure elements in subsequent vibration signal denoising, the performances of morphological filter with different structure elements...

A method based on 1-dimensional convolutional neural network(1-DCNN) was developed to identify the rail corrugation intelligently using the axle box acceleration. A mean of "spatial domain" cutting was proposed to make the sample set, and an appropriate 1-DCNN structure and configuration parameters were constructed, so that the data of the input sa...

Rail corrugation is very common in metro lines, which is one of the main causes of the fatigue damage of components of vehicle and track and considerable interior noise. Intelligent monitoring of rail corrugation is very necessary. A vehicle-track coupling dynamic model considering the flexibility of wheelset and track structure is established to s...

Wheel defects, such as pitting, generally occur on the tread of railway vehicle wheels. The measured wheel out-of-roundness (OOR) signals usually contain interference signals induced by pitting or other defects. The dimension of pitting is less than wheel-rail contact patch. The morphology filtering method is used to de-noise the wheel OOR signals....

A long-term wheel wear tracking test of an alpine high-speed EMU was carried out on a certain line. The evolution rule of wheel wear was obtained. The measured wheel profiles matched with CHN60 rail and 60N rail profiles, respectively. The influence of wheel wear on wheel-rail contact geometrical relationship and contact mechanical characteristics...