An Adaptive Filtering Method for Bridge Vibration Signals Based on Improved CEEMDAN and Multi-Scale Permutation Entropy

Aiming at the serious noise of bridge vibration signals in complex environment, this paper proposed an adaptive filtering and denoising optimization method for bridge structural health monitoring. The method took CEEMDAN algorithm as the core, during the step-by-step decomposition of original signals, white noise with opposite signs was added in each stage, meanwhile multi-scale permutation entropy (MPE) was introduced to analyze the mean entropy of each intrinsic mode function (IMF) at different scales, and components with serious noise were eliminated to complete the first filtering; then, in order to optimize the remaining IMFs for signal reconstruction and ensuring the smoothness and similarity of filtering, an optimized reconstruction model was established to complete the second filtering. Compared with the CEEMDAN method, the proposed method could solve the problems of mode mixing and endpoint effect with good completeness, orthogonality, and signal-to-noise ratio. At last, the advantages and application value of the proposed method were verified again by the vibration signal analysis of a real long-span bridge structure.

Vibration Signal Analysis of Chimney Blasting Based on HHT

In the blasting demolition processs of high-rise structures, the impact of blasting vibration to the environment and objects to be protected must be effectively controlled, so the blasting vibration signal is deeply analyzed [1]. In this paper, the blasting vibration signal of a chimney is analyzedbased on HHT. The blasting vibration signal is denoised by Empirical Mode Decomposition (EMD)-wavelet threshold, then using Hilbert-Huang Transform (HHT) [2] the measured blasting vibration waveform Hilbert spectrum, marginal spectrum and instantaneous energy graph are draw to analyze the chimney blasting vibration. The results show that the denoising effect of EMD-wavelet threshold is good for blasting vibration signal [3]. HHT method has a good feature identification ability when processing vibration signals, and can reflect the characteristics of data more comprehensively and accurately, which provides convenience for the study of vibration signal data.

An Expert System for Rotating Machine Fault Detection Using Vibration Signal Analysis

Accurate and early detection of machine faults is an important step in the preventive maintenance of industrial enterprises. It is essential to avoid unexpected downtime as well as to ensure the reliability of equipment and safety of humans. In the case of rotating machines, significant information about machine’s health and condition is present in the spectrum of its vibration signal. This work proposes a fault detection system of rotating machines using vibration signal analysis. First, a dataset of 3-dimensional vibration signals is acquired from large induction motors representing healthy and faulty states. The signal conditioning is performed using empirical mode decomposition technique. Next, multi-domain feature extraction is done to obtain various combinations of most discriminant temporal and spectral features from the denoised signals. Finally, the classification step is performed with various kernel settings of multiple classifiers including support vector machines, K-nearest neighbors, decision tree and linear discriminant analysis. The classification results demonstrate that a hybrid combination of time and spectral features, classified using support vector machines with Gaussian kernel achieves the best performance with 98.2% accuracy, 96.6% sensitivity, 100% specificity and 1.8% error rate.

Vibration signal analysis of planetary gear with amplitude, frequency and phase modulation

In this paper, the vibration signal of planetary gear with amplitude, frequency and phase modulation is studied. The proposed mathematical model is employed to in- vestigate the modulation behavior of planetary gear. Based on this model, the ampli- tude modulation (AM) sidebands are analyzed to verify the correctness of theoretical calculation by Inalpolat and Kahraman. Then, the frequency modulation (FM) side- bands and phase modulation (PM) sidebands are also illustrated through an exam- ple analysis. The effects of parameters of planetary gear such as number of planets, teeth of sun and planet phasing relationships on the AM, FM and PM sidebands are analyzed. Finally, the specific expression of transmission error, time-varying mesh stiffness and dynamic mesh force including gear manufacturing error is developed. Time history signal and acceleration spectra of gear mesh interface excitations including AM, FM and PM are investigated for the meshes of sun-planet and ring- planet. The results show that gear parameters have important influence on the mod- ulation behavior. Additionally, manufacturing errors can be introduced to predict the sidebands of planetary gear. The amplitude, frequency and phase modulation study are extremely significant for the noise and vibration reduction, especially the fault diagnosis of planetary gear

Fault detection and diagnosis using vibration signal analysis in frequency domain for electric motors considering different real fault types

Purpose Electric motors are present in most industries today, being the main source of power. Thus, detection of faults is very important to rise reliability, reduce the production cost, improving uptime and safety. Vibration analysis for condition-based maintenance is a mature technique in view of these objectives. Design/methodology/approach This paper shows a methodology to analyze the vibration signal of electric rotating motors and diagnosis the health of the motor using time and frequency domain responses. The analysis lies in the fact that all rotating motor has a stable vibration pattern on health conditions. If the motor becomes faulty, the vibration pattern gets changed. Findings Results showed that through the vibration analysis using the frequency domain response it is possible to detect and classify the motors in several induced operation conditions: healthy, unbalanced, mechanical looseness, misalignment, bent shaft, broken bar and bearing fault condition. Originality/value The proposed methodology is verified through a real experimental setup.