Wavelet Transform Based Methodology for Detection and Characterization of Multiple Oscillations in Nonstationary Variables

Naghoosi, Elham; Huang, Biao

doi:10.1021/acs.iecr.6b03075

Cited by 18 publications

(5 citation statements)

References 26 publications

(33 reference statements)

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“…A theoretical analysis of the properties underlying multivariate and multiscale statistical process control (MSSPC) can be found in [55]. Several other works report improvements or modifications made to the original base formulation [56][57][58][59][60] and a variety of applications of multiscale methods to process monitoring have been reported since then [61][62][63][64][65][66][67][68][69][70][71][72][73][74][75][76][77][78], including for the more complex case of batch processes [79,80]. More recently, image-based monitoring methods [81,82] were developed as well as methods dedicated to supervision of slowly evolving degradation phenomena, closely related to prognosis of equipment health and reliability [78,83,84].…”

Section: Multiscale Methods For Process Monitoringmentioning

confidence: 99%

Multiscale and Multi-Granularity Process Analytics: A Review

Reis

2019

Processes

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As Industry 4.0 makes its course into the Chemical Processing Industry (CPI), new challenges emerge that require an adaptation of the Process Analytics toolkit. In particular, two recurring classes of problems arise, motivated by the growing complexity of systems on one hand, and increasing data throughput (i.e., the product of two well-known “V’s” from Big Data: Volume × Velocity) on the other. More specifically, as enabling IT technologies (IoT, smart sensors, etc.) enlarge the focus of analysis from the unit level to the entire plant or even to the supply chain level, the existence of relevant dynamics at multiple scales becomes a common pattern; therefore, multiscale methods are called for and must be applied in order to avoid biased analysis towards a certain scale, compromising the benefits from the balanced exploitation of the information content at all scales. Also, these same enabling technologies currently collect large volumes of data at high-sampling rates, creating a flood of digital information that needs to be properly handled; optimal data aggregation provides an efficient solution to this challenge, leading to the emergence of multi-granularity frameworks. In this article, an overview is presented on multiscale and multi-granularity methods that are likely to play an important role in the future of Process Analytics with respect to several common activities, such as data integration/fusion, de-noising, process monitoring and predictive modelling, among others.

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Section: Multiscale Methods For Process Monitoringmentioning

confidence: 99%

Multiscale and Multi-Granularity Process Analytics: A Review

Reis

2019

Processes

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“…Naghoosi and Huang also proposed another method to discriminate three types of faults which are controller tuning-related faults, valve nonlinearities, and external disturbances for multiple oscillations. The method is based on WT as a nonlinearity detection technique and combined with ACF to further learn the type of the linear fault.…”

Section: Diagnosis Techniquesmentioning

confidence: 99%

Controller Performance Monitoring: A Survey of Problems and a Review of Approaches from a Data-Driven Perspective with a Focus on Oscillations Detection and Diagnosis

Bounoua

Aftab

Omlin

2022

Ind. Eng. Chem. Res.

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Optimal operations of industrial control systems require rigorous monitoring to ensure safety, increase profitability, and minimize plant maintenance downtime. Thus, controller performance monitoring has been actively pursued by the research community, resulting in increased research publications over the past two decades. The availability of large data sets, the so-called big data era, has led impetus to the data-driven domain of controller performance monitoring. In this paper, a comprehensive review of the data-driven research in the CPM domain is presented. To illustrate the rationale behind the research efforts, a succinct explanation of the faults observed in control loops and their influence on overall controller performance is also presented. The paper then provides the publicly available data repositories adopted by most researchers to assess and compare their performance monitoring techniques realistically. Moreover, a review of the most eminent techniques proposed so far concerning both detection and diagnosis phases is presented.

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“…For the attenuation of wind-induced noise which is in the same frequency band as the seismic waves, the wavelet threshold denoising method based on wavelet transform is selected, as it can preserve the signal integrity while removing the noise within the data. Wavelet transform has great advantages in processing non-stationary signals [32] and has been successfully applied in many fields including biomedical engineering [33], image processing [34], and signal analysis [35]. Besides, some researchers have also tried to apply the wavelet transform to the denoising and processing of land seismic signals and images [36][37][38].…”

Section: Seismic Signal Noise Attenuationmentioning

confidence: 99%

Shallow seafloor seismic wave monitoring using 3-component fiber optic interferometric accelerometer

Chang¹,

Yang²,

Luo³

et al. 2021

Meas. Sci. Technol.

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A fiber optic interferometric seismic monitoring system developed by us was deployed on the shallow seafloor near the Wuchang oceanic monitoring station off the Hainan Island, in the South China Sea, in 2017. In the past three plus years, it has recorded more than 40 teleseismic activities of magnitude 6.0+. We analyze these shallow seafloor observed seismic signals monitored by the fiber optic interferometric three-component accelerometer from the perspective of analyzing the underwater ambient seismic noise and the attenuation of the noise in the shallow seafloor seismic signals. The ambient seismic noise produced by wind, wave, shipping, and on-shore manmade activities admixed in the seismic signal is analyzed and discussed in detail. And the attenuation of such noise in the shallow seafloor seismic data using a hybrid denoising algorithm is demonstrated by applying it to three representative seismic events having different distances and magnitudes, with results showing that the noise in the shallow seafloor seismic signal is effectively reduced, and the ratio of the maximum acceleration value of seismic wave to the noise level is improved by more than 32 dB, rendering the seismic waves clearly observable.

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Wavelet Transform Based Methodology for Detection and Characterization of Multiple Oscillations in Nonstationary Variables

Cited by 18 publications

References 26 publications

Multiscale and Multi-Granularity Process Analytics: A Review

Multiscale and Multi-Granularity Process Analytics: A Review

Controller Performance Monitoring: A Survey of Problems and a Review of Approaches from a Data-Driven Perspective with a Focus on Oscillations Detection and Diagnosis

Shallow seafloor seismic wave monitoring using 3-component fiber optic interferometric accelerometer

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