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2017 | Buch

Structural Health Monitoring

An Advanced Signal Processing Perspective

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This book highlights the latest advances and trends in advanced signal processing (such as wavelet theory, time-frequency analysis, empirical mode decomposition, compressive sensing and sparse representation, and stochastic resonance) for structural health monitoring (SHM). Its primary focus is on the utilization of advanced signal processing techniques to help monitor the health status of critical structures and machines encountered in our daily lives: wind turbines, gas turbines, machine tools, etc. As such, it offers a key reference guide for researchers, graduate students, and industry professionals who work in the field of SHM.

Inhaltsverzeichnis

Frontmatter
Advanced Signal Processing for Structural Health Monitoring
Abstract
This chapter starts with an introduction on structural health monitoring (SHM) and emphasizes its importance for engineering systems. Then four different stages, i.e., operational evaluation, data acquisition, feature extraction and diagnosis and prognosis, involved in SHM are briefly discussed, followed by review of each signal processing technique used in SHM, which will be described in the book.
Ruqiang Yan, Xuefeng Chen, Subhas C. Mukhopadhyay
Signal Post-processing for Accurate Evaluation of the Natural Frequencies
Abstract
In this paper, a signal processing algorithm to accurately estimate the natural frequencies of structures for early recognition and assessment of cracks is proposed. In standard frequency estimation, the precision increases if the frequency resolution is improved. A finer resolution is achieved by increasing the analysis time interval. Nowadays, there are many other methods to improve the spectrum resolution, as the interpolation of spectral lines, zero-padding, zoom-FFT and so on. The proposed algorithm stepwise crops the acquired vibration signal and performs a spectral analysis. Superposing these spectra, an overlapped spectrum, with a dramatically increased resolution, results. This spectrum offers the possibility to identify very precisely the natural frequencies, even for damages in early stage. The algorithm was tested on generated and real-world signals and was proved to work well, even in the case of fast damped or short signals.
G. R. Gillich, I. C. Mituletu
Holobalancing Method and Its Improvement by Reselection of Balancing Object
Abstract
Based on the idea of multi-sensor information fusion, one of the main problem—insufficient utilization of rotor vibration information—existing in the traditional rotor balancing methods is solved. By integration of all the amplitude, frequency and phase information, the Holobalancing method can help to correct the rotor balancing state more accurately and efficiently than other traditional methods. The field balancing capability has been greatly improved therefore. Since the Holobalancing method truly considers the characteristics of system support stiffness anisotropy, the unreasonable isotropic assumption adopted in traditional balancing methods is no longer required therefore. The balancing result of the Holobalancing method is more reliable and fewer number of trial runs is needed. Recently, the Holobalancing method is further improved by reselection of balancing object. With the Initial Phase Vector (IPV) being replaced by its forward precession component (IPV+), the impact of probe orientation on the balancing analysis and calculation is completely eliminated and the computational procedure is greatly simplified without sacrificing the balancing accuracy. The experiments and field application cases verify the effectiveness and reliability of this method.
Yuhe Liao, Liangsheng Qu
Wavelet Transform Based on Inner Product for Fault Diagnosis of Rotating Machinery
Abstract
As a significant role in industrial equipment, rotating machinery fault diagnosis (RMFD) always draws lots of attention for guaranteeing product quality and improving economic benefit. But non-stationary vibration signal with a large amount of noise on abnormal condition of weak fault or compound fault in many cases would lead to this task challenging. As one of the most powerful non-stationary signal processing techniques, wavelet transform (WT) has been extensively studied and widely applied in RMFD. Many previous publications admit that WT can be realized by means of inner product principle of signal and wavelet base. This paper verifies the essence on inner product operation of WT by simulation experiments. Then the newer development of WT based on inner product is introduced. The construction and applications of major developments on adaptive multiwavelet in RMFD are presented. Finally, super wavelet transform as an important prospect of WT based on inner product are presented and discussed.
Shuilong He, Yikun Liu, Jinglong Chen, Yanyang Zi
Wavelet Based Spectral Kurtosis and Kurtogram: A Smart and Sparse Characterization of Impulsive Transient Vibration
Abstract
Mechanical signature analysis is of vital importance to the structural health monitoring of mechanical equipment. However, the fast development of mechanical signature analysis tool always requires a rich and deep understanding of state-of-the-art technologies, which is often lacked by the on-site staff. In this chapter, we introduce an effective methodology that ensure automatic detection of impulsive transient vibrations occurring during machinery fault events. This methodology is originally derived from the concept of spectral kurtosis, whose advent has a close relation with the early development of wavelet theory, and acquired a fast computation implementation named fast kurtogram. The essential originality of this methodology lies in its unique way of combining multi-scale analysis and scalar indicator based characterization of impulsive transient components. As a result, this methodology emerges as a single-input-single-output system for both theoretical researchers and on-site engineers. In the presented materials, basics and fundamentals of this fast developing methodology are introduced. The recent improvements mainly focus on the construction of new multi-scale signal decomposition frames and the invention of new scalar-valued indicators. All the efforts are motivated to obtain a satisfactory sparse characterization of impulsive transient components induced by machinery faults. A range of construction examples of wavelet-based spectral kurtosis with their engineering applications are presented to demonstrate the developments.
Binqiang Chen, Wangpeng He, Nianyin Zeng
Time-Frequency Manifold for Machinery Fault Diagnosis
Abstract
In this chapter a new method called time-frequency manifold (TFM) is reported for signature enhancement and sparse representation of non-stationary signals for machinery fault diagnosis. In the framework of the TFM analysis, the phase space reconstruction is firstly employed to reconstruct the dynamic manifold embedded in an analysed signal, then the time-frequency distributions (TFDs) are generated in the reconstructed phase space to represent the non-stationary information, and manifold learning is finally addressed on the TFDs to discover intrinsic TFM structure. In this process, the TFM combines non-stationary information and nonlinear information simultaneously. This will provide a better time-frequency signature with the merits of noise suppression and resolution enhancement for machine health diagnosis. Furthermore, a TFM synthesis approach is further reported to explicitly recover the transient signal from the TFM signature by combining the sparse theory with the TFM structure. The objective of the introduced work is to exploit a TFM technology for enhancing the time-frequency signature and representing the transient feature with in-band noise suppression for machine fault signature analysis and transient feature extraction.
Qingbo He, Xiaoxi Ding
Matching Demodulation Transform and Its Application in Machine Fault Diagnosis
Abstract
In this chapter, matching demodulation transform (MDT), an iterative algorithm, is introduced to generate a time-frequency (TF) representation with satisfactory energy concentration, and thus to extract the highly oscillatory frequency-modulation (FM) feature of rotating machine fault. As opposed to conventional time-frequency analysis (TFA) methods, this algorithm does not have to devise ad hoc parametric TF dictionary. Assuming the FM law of a signal can be well characterized by a determined mathematical model with reasonable accuracy, the MDT algorithm can adopt a partial demodulation and stepwise refinement strategy for investigating TF properties of the signal. The practical implementation of the MDT involves an iterative procedure that gradually matches the true instantaneous frequency (IF) of the signal. Moreover, because the MDT is a linear TFA method, it can reconstruct individual components from a multicomponent signal’s TF representation. Theoretical analysis of the MDT’s performance is provided, including quantitative analysis of the IF estimation error and the convergence condition. The validity and practical utility of the MDT is then demonstrated on simulation study, an experiment rotor system and a practical heavy oil catalytic cracking machine set with rotor rub-impact fault. The analysis results show that the MDT method is powerful in the analysis of FM signals and is an effective tool for the feature extraction of machine faults.
Xuefeng Chen, Shibin Wang
Compressive Sensing: A New Insight to Condition Monitoring of Rotary Machinery
Abstract
With the development of rotary machinery condition monitoring, challenges have often been encountered due to the cumbersome nature of data monitoring. Common methods in signal processing are primarily based on the Shannon sampling principle, which requires substantial amounts of data to achieve the desired accuracy from on-line monitoring signals. This limits their applications in cases for which only small samples can be collected, or cases for which too much data are generating which needs to be largely reduced with under-sampling. Using the Shannon sampling principle, it seems impossible to significantly reduce the quantity of data while preserving adequate useful information for condition monitoring. A newly developed theory termed compressive sensing provides a new insight to condition monitoring and fault diagnosis. It states that a signal can be perfectly recovered from under-sampled data, which means that useful condition information can still be represented by small samples. This study presents novel methods for rotary machinery fault detection from compressed vibration signals inspired by compressive sensing, which can largely reduce the data collection and detect faults of rotary machinery from only a few signal samples. This will greatly help reduce the amount of monitoring data while still guaranteeing a high accuracy of fault detection. Case studies related to roller bearing fault signals are also presented in this study to illustrate the effectiveness of the present strategy.
Gang Tang, Huaqing Wang, Yanliang Ke, Ganggang Luo
Sparse Representation of the Transients in Mechanical Signals
Abstract
This chapter focuses on the sparse representation of the transients in mechanical signals. Sparse representation means that the signal can be represented by an optimal linear combination of atoms by a specialized over-complete dictionary, leading to the sparsity of representation coefficients. Signal sparse representation consists of two main aspects, i.e., dictionary construction and optimization solution. This chapter also presents the applications of sparse representation, mainly in mechanical fault feature detection, such as fault detection of rolling bearings, gearboxes and compound bearing faults.
Zhongkui Zhu, Wei Fan, Gaigai Cai, Weiguo Huang, Juanjuan Shi
Fault Diagnosis of Rotating Machinery Based on Empirical Mode Decomposition
Abstract
Rotating machinery covers a broad range of mechanical equipment in industrial applications. It generally operates under tough working environment and is therefore subject to faults easily. Vibration signals collected in the working process have valuable contributions for the presentation of conditions of the rotating machinery. Consequently, using signal processing techniques, these faults could be detected and diagnosed. Empirical mode decomposition (EMD) is one of the most powerful signal processing techniques and has been widely applied in fault diagnosis of rotating machinery. This chapter attempts to introduce the recent research and development of EMD in fault diagnosis of rotating machinery, including basic concepts and fundamental theories about EMD methods and improved EMD methods. Moreover, the applications of EMD methods and improved EMD methods in fault diagnosis of common and key components of rotating machinery, like rotors, gears and rolling element bearings, are described in details.
Yaguo Lei
Bivariate Empirical Mode Decomposition and Its Applications in Machine Condition Monitoring
Abstract
Attributed to providing a more realistic representation of the signal without the artifacts imposed by non-adaptive limitations suffered by both Fourier- and Wavelet-transform based methods, Empirical Mode Decomposition (EMD) has been widely accepted as a favored tool for interpreting nonlinear, non-stationary signals, which are often associated with the occurrence of faults or variable operations of rotating machinery. In this chapter, the fundamental theory of the EMD will be explained. But more context will be spent on discussing its two dimensional form, namely Bivariate Empirical Mode Decomposition, and the powerful capacity of this innovative technique in the application of machine condition monitoring.
Wenxian Yang
Time-Frequency Demodulation Analysis Based on LMD and Its Applications
Abstract
A time-frequency demodulation technique based on local mean decomposition (LMD) is proposed for rotating machine diagnosis. In addition, methods for boundary processing and for determining the step size of the moving average are presented to improve LMD algorithm. Instantaneous amplitude (IA) and instantaneous frequency (IF) of the signal can be achieved using the improved LMD method. A well-constructed description of the derived IAs and IFs is represented in the form of instantaneous time-frequency spectrum (ITFS), which preserves both the time and frequency information simultaneously. Results of three synthetic signals indicate that the proposed method is much better in extracting the comprehensive carrier and modulated components, compared with Hilbert-Huang transform and stationary wavelet transform. The validity of the technique is further demonstrated on the rotor system and a gearbox. The transient fluctuations of the IF and the impulsive signatures can be successfully identified in the ITFS. Moreover, it has been demonstrated that the proposed time-frequency demodulation technique is much more effective and sensitive than the other methods in detecting impulsive and modulated components.
Yanxue Wang, Xuefeng Chen, Yanyang Zi
On the Use of Stochastic Resonance in Mechanical Fault Signal Detection
Abstract
This chapter focuses on the application of stochastic resonance (SR) in mechanical fault signal detection. SR is a nonlinear effect that is now widely used in weak signal detection under heavy noise circumstances. In order to extract characteristic fault signal of the dynamic mechanical components, SR normalized scale transform is presented and a circuit module is designed based on parameter-tuning bistable SR. Weak signal detection based on stochastic resonance (SR) can hardly succeed when noise intensity exceeds the optimal value of SR. Therefore, a signal detection model based on combination effect of colored noise SR and parallel bistable SR array, which is called multi-scale bistable stochastic resonance array, has been constructed. Based on the enhancement effect of the constructed model and the normalized scale transformation, weak signal detection method has been proposed. The effectiveness of these methods are confirmed and replicated by numerical simulations. Applications of bearing fault diagnosis show the enhanced detecting effects of the proposed methods.
X. F. Zhang, N. Q. Hu, L. Zhang, X. F. Wu, L. Hu, Z. Cheng
Metadaten
Titel
Structural Health Monitoring
herausgegeben von
Ruqiang Yan
Xuefeng Chen
Subhas Chandra Mukhopadhyay
Copyright-Jahr
2017
Electronic ISBN
978-3-319-56126-4
Print ISBN
978-3-319-56125-7
DOI
https://doi.org/10.1007/978-3-319-56126-4

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