This paper concerns the track defects detection issue with the aid of bogie acceleration measured from the in-service vehicle. The detection principle and the acceleration sensors are introduced in the first place. To validate the feasibility of the detection method, a vehicle-track dynamic model with different defects of corrugations and squats on the track is built first by using SIMPACK. The bogie acceleration (BA) collected by the acceleration sensor installed on the four corners of bogie frame is measured for the defects detection. Then time-frequency analysis is proceeded to extract time-frequency features of the various track defects, the simulation result indicates that the proposed method can detect the track defects in the light of the feature frequencies derived from the continuous wavelets analysis and the feature modes from the empirical mode decomposition (EMD) method. After that, field test experiments are carried out at Shanghai metro Line 1. The results show that the proposed method can detect squats and corrugation defects by using the measured bogie acceleration from the in-service train successfully.

This article addresses the fault detection observer design issue for linear time invariant (LTI) systems with additive or multiplicative uncertainties, which are also subject to unknown disturbances. The observer design is investigated under the ℋ_∞/ℋ₋ index framework using the generalised KYP lemma in the finite-frequency domain. Sufficient conditions for the existence of such a fault detection observer are given in terms of linear matrix inequalities (LMIs). The threshold design issue is discussed and a method for estimating the worst undetectable fault size is proposed. The effectiveness of the proposed algorithms is illustrated by numerical simulation examples.

This paper addresses the robust fault detection observer and fault estimation filter design issues for linear time invariant (LTI) systems with parameter uncertainties in a polytope and the systems are subject to unknown inputs as well. The observer and filter design are investigated in the H_∞/H_– index framework in the finite frequency interval by using the generalized KYP lemma. The threshold design and the worst undetectable fault size estimation are discussed. A numerical example is given to illustrate the effectiveness of the derived algorithms.

The identification and assessment of rail corrugation are two of the essential tasks of daily railway inspection to guarantee the safety of train operation and promote the development of an efficient maintenance strategy. In view of the requirements for automatic identification and smart decision-making, computer vision-based rail corrugation identification and assessment methods are presented in this paper. Firstly, an improved Spatial Pyramid Matching (SPM) model, integrating multi-features and locality-constrained linear coding (IMFLLC), is proposed for rail corrugation identification. After that, an innovative period estimation method for rail corrugation is proposed based on the frequency domain analysis of each column in the corrugation region. Finally, the severity of the rail corrugation is assessed with the help of the wear saliency calculation and fuzzy theory. The experiment results demonstrate that the proposed corrugation identification method achieves a higher precision rate and recall rate than those of traditional methods, reaching 99.67% and 98.34%, respectively. Besides, the validity and feasibility of the proposed methods for the rail corrugation period estimation and severity assessment are also investigated.