A two-level adaptive chirp mode decomposition and tangent entropy in estimation of single-valued neutrosophic cross-entropy for detecting impeller defects in centrifugal pump

Vashishtha, Govind; Chauhan, Sumika; Yadav, Nitin; Kumar, Anil; Kumar, Rajesh

doi:10.1016/j.apacoust.2022.108905

Cited by 40 publications

(16 citation statements)

References 50 publications

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“…In 2020, to tackle the problem of oscillation detection and fault diagnosis, a fast ACMD (FACMD) algorithm was established to process complex multiple oscillations, where the running time of the proposed FACMD algorithm is reduced greatly by setting a reasonable stop threshold compared with the vanilla ACMD . Currently, in 2022, a two-level ACMD (TL-ACMD) is reported by incorporating the tangent energy and calculating energy eigenvalues of each mono-mode for decomposing the acoustic data of the impeller defects in a centrifugal pump (Vashishtha et al, 2022). Although the decomposition methods listed above have some promising advantages in the simulated domain and industrial areas, the common shortcoming of those decomposition methods is their poor capacity for separating each mono-component individually from a complicated multi-component with severely crossed IFs characteristics or overlapped components.…”

Section: Literature Reviewmentioning

confidence: 99%

Spatio-temporal nonconvex penalty adaptive chirp mode decomposition for signal decomposition of cross-frequency coupled sources in seafloor dynamic engineering

2022

Front. Mar. Sci.

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Electromagnetic field noise and clutter generated from the motion of ocean waves are the main obstacles in the research of magnetotelluric dynamic analysis, and it is difficult to extract the crossed instantaneous frequencies (IFs) of underwater electromagnetic detected (UEMD) data due to the limited resolution of the current time-frequency techniques. To alleviate this bottleneck issue, a new spatio-temporal nonconvex penalty adaptive chirp mode decomposition (STNP-ACMD) is originally proposed for separating each mono-component individually from a complicated multi-component with severely crossed IFs or overlapped components, in this paper. Specifically, the idea of a nonconvex penalty greedy strategy is incorporated into the vanilla ACMD method by using a recursive mode extraction scheme, and the fractional-order characteristic of the observation signal is also considered. Meanwhile, the spatio-temporal matrices were constructed elaborately and then applied to capture coupling characteristics and spatio-temporal relationships among all estimated mono-components. Eventually, a high-resolution adaptive time-frequency spectrum is obtained according to the IFs and instantaneous amplitudes (IAs) of each estimated mono-component. The effectiveness and practicability of the proposed algorithm were verified via simulated scenarios and velocity dynamic data of the seafloor from the South China Sea, compared with four state-of-the-art benchmarks.

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Section: Literature Reviewmentioning

confidence: 99%

Spatio-temporal nonconvex penalty adaptive chirp mode decomposition for signal decomposition of cross-frequency coupled sources in seafloor dynamic engineering

2022

Front. Mar. Sci.

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“…Yan et al [17] combined VMD mapping MFCC and LSTM to detect diesel engine faults. Vashishtha et al [18] extracted the single-valued neutrosophic cross-entropy of adaptive chirp mode decomposition based on acoustic signal to identify the impeller defects in the centrifugal pump.…”

Section: Introductionmentioning

confidence: 99%

Construction of multi-features comprehensive indicator for machinery health state assessment

Ding,

Wei,

Wang

et al. 2024

Meas. Sci. Technol.

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Health state assessment is critical for mechanical equipment’s smooth and healthy operation. This paper proposes a novel approach for health state assessment based on acoustic signals during the process of machinery running. It consists of multi-domain feature (MF) extraction and comprehensive health indicator (CHI) construction. MF is extracted from various acoustic features, including time and frequency (TF) features, Mel-Frequency Cepstral Coefficients (MFCC), and Gammatone Frequency Cepstral Coefficients (GFCC). The stacked long short-term memory (LSTM) is used to extract the high-level features of the MF, which are then input to the downstream PCA to obtain the LSTM-PCA health indicator (LP-HI). Parallelly, the MF is fed into the Self-Organizing Mapping (SOM) model to calculate the minimum quantization error (MQE) as SOM-MQE health indicator (SM-HI). These two indicators are fused using weighted fusion and nonlinear mapping to calculate CHI. The experimental results on Air Compressor Dataset show a 25.8% reduction in evaluation error compared with SOTA results in this paper. The proposed nonlinear mapping function furthermore reduces fitting error on HI by 38.9%. These demonstrate the effectiveness and superiority of the proposed method in machinery health state assessment.

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“…ACMD is mostly used for fault identification of mechanical bearing structures because of its superiority in handling compared with primitive, unsteady AE [ 13 , 14 , 15 ]. Govind Vashishtha et al demonstrated a new method for identifying defects in centrifugal pump impellers through ACMD [ 16 ]. Chuancang Ding and Baoxiao Wang proposed an improved Sparsity-ACMD for the detection of rub-impact faults [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

A Wireless Acoustic Emission Sensor System with ACMD-IGWO-XGBoost Algorithm for Living Tree Moisture Content Diagnosis

Yang

Yin

Liu

2023

Plants

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Trunk water has an important influence on the metabolism and ecological balance of living trees, which affects the vegetation growth and moisture cycle of the whole forest ecosystem. The accurate and real-time measurement of moisture content (MC) is of vital guiding meaning to living tree cultivation and forest management. In this paper, a water content diagnosis system based on a wireless acoustic emission sensor network (WASN) was designed and implemented with the aim of the nondestructive detection of water content in living wood trunks. Firstly, the acoustic emission (AE) signal of the trunk epidermis was sampled at high speed; then, its characteristic parameters were calculated and transmitted wirelessly to the gateway. Furthermore, the optimal characteristic wavelet sequence was decomposed by the adaptive chirp mode decomposition (ACMD), and the improved grey wolf optimizer (IGWO) optimization XGBoost established the MC prediction model, which was improved by the multi-strategy joint optimization. Finally, field monitoring was carried out on Robinia Pseudoacacia, Photinia serrulata, Pinus massoniana and Toona sinensis. The average diagnostic accuracy reached 96.75%, which shows that the diagnosis system has excellent applicability in different working conditions.

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A two-level adaptive chirp mode decomposition and tangent entropy in estimation of single-valued neutrosophic cross-entropy for detecting impeller defects in centrifugal pump

Cited by 40 publications

References 50 publications

Spatio-temporal nonconvex penalty adaptive chirp mode decomposition for signal decomposition of cross-frequency coupled sources in seafloor dynamic engineering

Spatio-temporal nonconvex penalty adaptive chirp mode decomposition for signal decomposition of cross-frequency coupled sources in seafloor dynamic engineering

Construction of multi-features comprehensive indicator for machinery health state assessment

A Wireless Acoustic Emission Sensor System with ACMD-IGWO-XGBoost Algorithm for Living Tree Moisture Content Diagnosis

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