Real-time monitoring of complex sensor data using wavelet-based multiresolution analysis

Contraposing the surface quality testing of a Wolter-I type mandrel after ultraprecise turning, we propose a measuring method based on a contact-type profilometer and fabricate an adjusting device to assist in measurements. By coordinating probe translational motion in the Z and Y directions, this device provides 6 degrees of freedom. Extracting the feature point on the curve of a meridian section contour, the measurement data of the two aspherical surfaces that constitute a Wolter-I type mandrel can be segregated. This feature point is used as a reference point to make the measurement coordinate and machine coordinate systems coincide with each other in order to calculate the dimensional errors of the mandrel. By choosing samples of different lengths, some property indexes of the mandrel surface, such as roughness, waviness, and form error, can be extracted from the contour error signals via the wavelet transform method. This proposed method can solve the problem that the aspheric surface dimension error and contour form error of the Wolter-I mandrel surface cannot be measured by the profilometer when the aspheric reference vertex cannot be determined. Using this method, only one measuring instrument is required to evaluate the mandrel after diamond turning, which can improve the measurement efficiency, and cover the full spatial wavelength range from the figure error to the surface roughness. In addition, this method has the potential to achieve the in situ measurement of the surface of the Wolter-I type ultra-precision mandrel.

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“…Therefore, the wavelet transform is more suitable to segregate the features of the mandrel surface contour. 24–26…”

Section: Measurement Methodologymentioning

confidence: 99%

Measurement method of Wolter-I type mandrel based on a contact-type profilometer

Kong

Sun

Geng

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part B:

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“…For the sake of determining fault trend control limit 1 , wavelet filtering is firstly introduced as a preprocessing tool to historical normal observation data, and then DCA is used to perform feature extraction to the filtered normal observation. The filtering process can be described as follows [32,33].…”

Section: Journal Of Control Science and Engineeringmentioning

confidence: 99%

DCA-Based Real-Time Residual Useful Life Prediction for Critical Faulty Component

Zhou

Wang

Gao

2017

Journal of Control Science and Engineering

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Residual useful life (RUL) prediction is significant for condition-based maintenance. Traditional data-driven RUL prediction method can only predict fault trend of the system rather than RUL of a specific system component. Thus it cannot tell the operator which component should be maintained. The innovation of this paper is as follows: (1) Wavelet filtering based method is developed for early detection of slowly varying fault. (2) Designated component analysis is introduced as a feature extraction tool to define the fault precursor of a specific component. (3) Exponential life prediction model is established by nonlinear fitting of the historical RUL and the fault size characterized by the statistics used. Once online detection statistics is obtained, real-time RUL of the critical component can be predicted online. Simulation shows the effectiveness of this algorithm.

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“…Multiscale representation has been shown to effectively deal with real process data as it allows efficient separation of important features from stochastic noise and provides wavelet coefficients that are approximately decorrelated and more Gaussian at multiple scales. Thus, it can help address most of the assumptions of the conventional univariate fault detection or control charts [24,25]. These advantages of multiscale representation will be utilized in this work to develop a multiscale Shewhart chart algorithm that will provide improved performance.…”

Section: Introductionmentioning

confidence: 99%

Systematic Preparation of Artificial Cells (DNA Crown Cells)

Inooka¹

2017

J Chem Eng Process Technol

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Process safety is a critical component in various process industries. Statistical process monitoring techniques were initially developed to maximize efficiency and productivity, but over the past few decades with catastrophic industrial disasters, process safety has become a top priority. Sensors play a crucial role in recording process measurements, and according to the number of monitored variables, process monitoring techniques can be classified into univariate or multivariate techniques. Most univariate process monitoring techniques rely on three fundamental assumptions: that process residuals contain a moderate level of noise, are independent, and are normally distributed. Practically, however, due to a variety of reasons such as modeling errors and malfunctioning sensors, these assumptions are violated, which can lead to catastrophic incidents. Fortunately, multiscale wavelet-based representation of data inherently possesses characteristics that are able to deal with these violations of assumptions. Therefore, in this work, multiscale representation is utilized to enhance the performance of the Shewhart chart (which is a well-known univariate fault detection method) to help improve its performance. The performance of the developed multiscale Shewhart chart was assessed and compared to the conventional chart through two examples, one using synthetic data, and the other using simulated distillation column data. The results of both examples clearly show that the developed multiscale Shewhart chart provides lower missed detection and false alarm rates, as well as lower ARL 1 values (i.e., quicker detection) for most cases where the fundamental assumptions of the Shewhart chart are violated. Additionally, the relative simplicity of the proposed algorithm encourages its implementation in practice to help improve process safety.

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Real-time monitoring of complex sensor data using wavelet-based multiresolution analysis

Cited by 8 publications

References 32 publications

Measurement method of Wolter-I type mandrel based on a contact-type profilometer

Measurement method of Wolter-I type mandrel based on a contact-type profilometer

DCA-Based Real-Time Residual Useful Life Prediction for Critical Faulty Component

Systematic Preparation of Artificial Cells (DNA Crown Cells)

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