Multiscale noise tuning stochastic resonance enhances weak signal detection in a circuitry system

Dai, Daoyi; He, Qingbo

doi:10.1088/0957-0233/23/11/115001

Cited by 25 publications

(17 citation statements)

References 23 publications

(49 reference statements)

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“…When the MSTSR and the SMSTSR methods are conducted to deal with the same offline signals, it can be found that both of the two methods outperform the EMD-based and the LPSR-based spectral analysis methods in processing three kinds of defective signals. The obtained experimental results show that the weak periodic signals have been enhanced successfully by the 1/ f -type noise [17]- [19]. From the running duration aspect, it can be found that the EMD method spends a large computation time but has poorer efficiency.…”

Section: B Effectiveness Of the Proposed Algorithmmentioning

confidence: 87%

“…The bandwidth w j of each Filter j corresponds to the multiscale property of orthonormal WT [17]. That is as follows:…”

Section: B Filter Arraymentioning

confidence: 99%

“…In [17]- [19], the multiscale noise tuning SR (MSTSR) has been proposed as a new kind of large parameter SR (LPSR). The MSTSR method has been verified to outperform traditional SR method and some other signal processing methods for enhancing weak signals being submerged in heavy noise in bearing fault diagnosis [18], [19].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Sequential Multiscale Noise Tuning Stochastic Resonance for Train Bearing Fault Diagnosis in an Embedded System

et al. 2014

IEEE Trans. Instrum. Meas.

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Multiscale noise tuning stochastic resonance (MSTSR) has been proved to be an effective method for enhanced fault diagnosis by taking advantage of noise to detect the incipient faults of the bearings and gearbox. This paper addresses a sequential algorithm for the MSTSR method to detect the train bearing faults in an embedded system through the acoustic signal analysis. Specifically, the energy operator, digital filter array, and fourth rank Runge-Kutta equation methods are designed to realize the signal demodulation, multiscale noise tuning, and bistable stochastic resonance in sequence. The merit of the sequential algorithm is that it reduces the memory consumption and decreases the computation complexity, so that it can be efficiently implemented in the embedded system based on a low-cost, low-power hardware platform. After the sequential algorithm, the real-valued fast Fourier transform is used to calculate the power spectrum of the analyzed signal. The proposed method has been verified in algorithm performance and hardware implementation by three kinds of practical acoustic signals from defective train bearings. An enhanced performance of the proposed fault diagnosis method is confirmed as compared with several traditional methods, and the hardware performance is also validated.Index Terms-Embedded system, fault diagnosis, multiscale noise tuning, sequential algorithm, stochastic resonance (SR), train bearing.

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Section: B Effectiveness Of the Proposed Algorithmmentioning

confidence: 87%

“…The bandwidth w j of each Filter j corresponds to the multiscale property of orthonormal WT [17]. That is as follows:…”

Section: B Filter Arraymentioning

confidence: 99%

See 1 more Smart Citation

Sequential Multiscale Noise Tuning Stochastic Resonance for Train Bearing Fault Diagnosis in an Embedded System

et al. 2014

IEEE Trans. Instrum. Meas.

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“…Since the concept of SR was first put forward by Benzi et al in 1981 [1], the positive effect of SR has been widely investigated and applied in various research fields, such as physics, chemical, biological and electronic, etc. [2][3][4][5][6][7][8][9][10]. In signal detection theory, it is also called noise enhanced detection [3].…”

Section: Introductionmentioning

confidence: 99%

Noise Enhanced Signal Detection of Variable Detectors under Certain Constraints

Yang

Liu

et al. 2018

Entropy

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In this paper, a noise enhanced binary hypothesis-testing problem was studied for a variable detector under certain constraints in which the detection probability can be increased and the false-alarm probability can be decreased simultaneously. According to the constraints, three alternative cases are proposed, the first two cases concerned minimization of the false-alarm probability and maximization of the detection probability without deterioration of one by the other, respectively, and the third case was achieved by a randomization of two optimal noise enhanced solutions obtained in the first two limit cases. Furthermore, the noise enhanced solutions that satisfy the three cases were determined whether randomization between different detectors was allowed or not. In addition, the practicality of the third case was proven from the perspective of Bayes risk. Finally, numerous examples and conclusions are presented.

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“…In recent years, this phenomenon has attracted considerable attention for information transmission and detection [12,13,14,15], biological neuro analysis [16,17,18,19], and image processing [20,21,22,23]. In the mechanical engineering field, it has been theoretically investigated whether stochastic resonance can be applied to improve the efficiency of energy harvesting [24].…”

Section: Introductionmentioning

confidence: 99%

Effectiveness Testing of a Piezoelectric Energy Harvester for an Automobile Wheel Using Stochastic Resonance

Zhang

Zheng

Shimono

et al. 2016

Sensors

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The collection of clean power from ambient vibrations is considered a promising method for energy harvesting. For the case of wheel rotation, the present study investigates the effectiveness of a piezoelectric energy harvester, with the application of stochastic resonance to optimize the efficiency of energy harvesting. It is hypothesized that when the wheel rotates at variable speeds, the energy harvester is subjected to on-road noise as ambient excitations and a tangentially acting gravity force as a periodic modulation force, which can stimulate stochastic resonance. The energy harvester was miniaturized with a bistable cantilever structure, and the on-road noise was measured for the implementation of a vibrator in an experimental setting. A validation experiment revealed that the harvesting system was optimized to capture power that was approximately 12 times that captured under only on-road noise excitation and 50 times that captured under only the periodic gravity force. Moreover, the investigation of up-sweep excitations with increasing rotational frequency confirmed that stochastic resonance is effective in optimizing the performance of the energy harvester, with a certain bandwidth of vehicle speeds. An actual-vehicle experiment validates that the prototype harvester using stochastic resonance is capable of improving power generation performance for practical tire application.

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Multiscale noise tuning stochastic resonance enhances weak signal detection in a circuitry system

Cited by 25 publications

References 23 publications

Sequential Multiscale Noise Tuning Stochastic Resonance for Train Bearing Fault Diagnosis in an Embedded System

Sequential Multiscale Noise Tuning Stochastic Resonance for Train Bearing Fault Diagnosis in an Embedded System

Noise Enhanced Signal Detection of Variable Detectors under Certain Constraints

Effectiveness Testing of a Piezoelectric Energy Harvester for an Automobile Wheel Using Stochastic Resonance

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