Jiawei Xiang

Identification of damage locations based on operating deflection shape

Vibration-based damage detection methods have attracted a lot of investigations. Damage detection using natural frequencies or mode shapes is an efficient way to detect damages in static structures, but it cannot be directly extended to operating structures. In this paper, we present a new method based on operating deflection shape (ODS). The ODS of a damaged plate-like structure excited by a harmonic force is calculated using wavelet numerical method. The performance of wavelet numerical method is verified to analyse eigenvalue problem. Based on high-performance numerical simulation, the possibility of ODS in the prediction of damage locations is then investigated. Two typical plates with damages are given and the corresponding ODS are analysed using interval wavelet transform. It is found that the predicted results are in good agreement with the actual results and noise immunity of the method is relative robustness. Moreover, the interval wavelet transform has the ability to decrease boundary distortion phenomenon for the finite-length data of ODS. The present method provides a new way to detect damages in structures for operating conditions.

Structural Dynamical Monitoring and Fault Diagnosis

1College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, China 2School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China 3Department of Mechanical Engineering, Technology University of Darmstadt, 64289 Darmstadt, Germany 4Department of Structural Engineering, University of California, San Diego, CA 0085, USA 5Department of Civil Engineering, University of Siegen, 57076 Siegen, Germany

A multi-sensor fault detection strategy for axial piston pump using the Walsh transform method

The axial piston pump is a key component of the industrial hydraulic system, and the failure of pump can result in costly downtime. Efficient fault detection is very important for improving reliability and performance of axial piston pumps. Most existing diagnosis methods only use one kind of the discharge pressure, vibration, or acoustic signal. However, the hydraulic pump is a typical mechanism–hydraulics coupling system, all of the pressure, vibration, and acoustic signals contain useful information. Therefore, a novel multi-sensor fault detection strategy is developed to realize more effective diagnosis of axial piston pump. The presence of periodical impulses in these signals usually indicates the occurrence of faults in pump. Unfortunately, in the working condition, detecting the faults is a difficult job because they are rather weak and often interfered by heavy noise. Therefore, noise suppression is one of the most important procedures to detect the faults. In this article, a new denoising method based on the Walsh transform is proposed, and the innovation is that we use the median absolute deviation to estimate the noise threshold adaptively. Numerical simulations and experimental multi-sensor data collected from normal and faulty pumps are used to illustrate the feasibility of the proposed approach.