An enhanced PCA method with Savitzky-Golay method for VRF system sensor fault detection and diagnosis

In industrial production, it is highly essential to extract faults in gearbox accurately. Specifically, in a strong noise environment, it is difficult to extract the fault features accurately. LMD (local mean decomposition) is widely used as an adaptive decomposition method in fault diagnosis. In order to improve the mode mixing of LMD, ELMD (ensemble Local Mean Decomposition) is proposed as local mode mixing exists in noisy environment, but white noise added in ELMD cannot be completely neutralized leading to the influence of increased white noise on PF (product function) component. This further leads to the increase in reconstruction errors. Therefore, this paper proposes a composite fault diagnosis method for gearboxes based on an improved ensemble local mean decomposition. The idea is to add white noise in pairs to optimize ELMD, defined as CELMD (Complementary Ensemble Local Mean Decomposition) then remove the decomposed high noise component by PE (Permutation Entropy) while applying the SG (Savitzky-Golay) filter to smooth out the low noise in PFs. The method is applied to both simulated signal and experimental signal, which overcomes mode mixing phenomenon and reduces reconstruction error. At the same time, this method avoids the occurrence of pseudocomponents and reduces the amount of calculation. Compared with LMD, ELMD, CELMD, and CELMDAN, it shows that improved ensemble local mean decomposition method is an effective method for extracting composite fault features.

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“…The Savitzky-Golay filter is widely used for data stream smoothing and noise reduction. The specific algorithm is as follows [37].…”

Section: Sg Filteringmentioning

confidence: 99%

Application of an Improved Ensemble Local Mean Decomposition Method for Gearbox Composite Fault Diagnosis

Wang

Cai

et al. 2019

Complexity

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“…SG algorithm can filter out the noise while keeping the shape of the signal unchanged, that is, it protects the static features very well. It is a filtering method based on least square fitting, which is widely applied in signal denoising and data flow smoothing [39]- [42], its introduction is as follows.…”

Section: Savitky-golay Filtermentioning

confidence: 99%

A Temperature Compensation Approach for Dual-Mass MEMS Gyroscope Based on PE-LCD and ANFIS

Cao

Wei

Liu³

et al. 2021

IEEE Access

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Because the dual-mass MEMS gyroscope's output is greatly influenced by temperature, which can lead to errors that cannot be ignored. To solve this problem, a novel compensation method is proposed: a parallel processing algorithm, which integrates the Permutation entropy (PE), Local Characteristic-scale Decomposition (LCD) and Adaptive network-based fuzzy inference system (ANFIS). Firstly, LCD is used to decompose the output which contains temperature noises and drifts into a trend component and several intrinsic scale components (ISC), according to autocorrelation and complexity, three different categories will be obtained by PE: pure noise output, mixed output, and drift output. The different processes are as follows, the noise output is discarded, the mixed output is filtered by SG (Savitzky-Golay filter), then dual ANFIS is applied. Since the drift output completely reflects the temperature characteristics, the degree of non-linearity is high, the ANFIS with complex rules is used for processing. And the mixed output is composed of intermediate layer modes, containing a relatively small amount of temperature characteristics, simple rule ANFIS is adopted for processing. Finally, the signal is reconstructed. After that, the temperature error experiment is carried out, the result shows the method can effectively eliminate the error and compensate for the drift, it has a fast convergence speed and good effect, and has the advantage of good compensation efficiency.

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“…Polynomial smoothing algorithm is based on least squares principle, also known as convolution smoothing. In [26], the algorithm is described in detail. Take m points near the original data x and set x as the origin.…”

Section: Sg Filteringmentioning

confidence: 99%

Crow Search Algorithm for MEMS Gyroscope Temperature Drift Signal and Processing for Denoising

Wang

Cao

Duan

2021

Shock and Vibration

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To solve the problem of micro-electro-mechanical system (MEMS) gyroscope noise, this paper presents a variational mode decomposition (VMD) method based on crow search algorithm. First, the signal was decomposed by variational mode decomposition for optimization of crow search algorithm (CSA-VMD) method. The parameters required by the VMD method (penalty parameter α and decomposition number K) are given by the crow search algorithm, and then the signal is decomposed into the superposition of multiple subsignals, called intrinsic mode functions (IMFs). The sample entropy (SE) corresponding to each IMF is then obtained. By calculating the sample entropy, the noise signal can be divided into pure noise part, mixing part, and temperature drift part. Second, Savitzky–Golay smoothing denoising (SG) is used to filter the mixed noise signal to eliminate the influence of noise. Third, for the filtering of the drift part, the least square support vector machine optimized by the crow search algorithm (CSA-LSSVM) was used to filter, so as to reduce the effect of temperature drift. Finally, the processed signal is reconstructed to achieve the goal of denoising. Through the results, it can be found that the optimized VMD and LSSVM using CSA algorithm can achieve more effective denoising. After using the method proposed in this paper, the angular random walk value is 1.1175 ∗ 10−4°/h/√Hz, and the bias stability is 0.0017°/h. Compared with the original signal, the two signals are optimized by 98.1% and 98.2%, respectively. It can be seen from the experimental results that the proposed CSA-VMD method, SG method, and CSA-LSSVM method can effectively eliminate noise effects.

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An enhanced PCA method with Savitzky-Golay method for VRF system sensor fault detection and diagnosis

Cited by 65 publications

References 27 publications

Application of an Improved Ensemble Local Mean Decomposition Method for Gearbox Composite Fault Diagnosis

Application of an Improved Ensemble Local Mean Decomposition Method for Gearbox Composite Fault Diagnosis

A Temperature Compensation Approach for Dual-Mass MEMS Gyroscope Based on PE-LCD and ANFIS

Crow Search Algorithm for MEMS Gyroscope Temperature Drift Signal and Processing for Denoising

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