Pressure Vessel Diagnosis by Eliminating Undesired Signal Sources and Incorporating GA-Based Fault Feature Evaluation

Abstract

This paper proposes a reliable fault detection model for a pressure vessel under low pressure conditions. To improve the diagnostic performance, signals of different vessel health conditions are purified by eliminating noise so that signals of different categories are much more distinguishable. This de-noising technique uses a blind source separation (BSS) technique in which an initial noise-contaminated signal is separated into constituent sources. These individual sources are either device status-characterizing signal sources or interfering sources. Noise is removed and an unimpaired signal is regenerated from the characteristic sources. An abundant pool of heterogenous features are extracted from a refined signal to avoid omitting important fault-related properties. However, some of these fault features may be redundant and irrelevant, and they are likely to cause classification performance degradation. To choose the most discriminative fault-signatures from a plentiful feature set as well as reduce the dimensions of the feature input, this study proposes a new feature selection algorithm that associates a genetic algorithm (GA)-based discriminative feature analysis to a k-nearest neighbors (k-NN) classifier. The efficacy of involved techniques and the overall fault diagnostic model is examined in terms of visual and qualitative evaluations. Experimental results illustrated in this study justify that the proposed fault detection model is promising and outstanding compared to other state-of-the-art counterparts.

Multistage Centrifugal Pump Fault Diagnosis by Selecting Fault Characteristic Modes of Vibration and Using Pearson Linear Discriminant Analysis

“…Eq. 15shows the out-of-plane vibration of mechanical seal, where h and t are the cross-sectional height and thickness of the ring, d is the diameter, c is the torsion constant, v is Poisson's ratio, and n represents the mode of vibration in (15) and (16). It is worth noting that the fundamental and in-plane vibrations occur at high frequencies.…”

“…However, in the case of mechanical seal, lower frequencies can be found at out-ofplane bending modes of vibrations. Mechanical seal out-ofplane bending, or flexural vibration, and its consecutive modes are calculated using (15). It can be observed from Figures 6(a), 6(b), and 6(c) that the CP excitation frequency appears in between the 2nd and 3rd mode of flexural vibration with an amplitude almost double that of the normal condition whenever a mechanical seal defect occurs.…”

“…To evaluate the quality of the proposed CP fault diagnosis strategy, this study compares the proposed method with one vibration signal features preprocessing technique [15], one supervised dimensionality reduction technique (Tr-LDA) [38], and one unsupervised dimensionality reduction technique (PCA) [39]. To make the comparison fair and to ensure repeatability in results, the experiments were performed 15 times with a random combination of training and testing data.…”

“…The method of Viet et al [15] first preprocesses the raw signal by removing the noise using the blind source separation (BSS) technique. Furthermore, raw hybrid features are extracted from the denoised signal in time, frequency, and the time-frequency domain.…”

“…Spectral kurtosis focuses on impulsiveness, which makes it sensitive to the outliers or periodic noises. Viet et al [15] used blind source separation techniques for noise reduction in the acoustic emission signals for pressure vessel diagnosis. However, these kinds of techniques need a baseline or reference signals to ensure the appropriate noise reduction in the unimpaired signals.…”