A second-order stochastic resonance method enhanced by fractional-order derivative for mechanical fault detection

Qiao, Zijian; Elhattab, Ahmed; Shu, Xuedao; He, Chunlin

doi:10.1007/s11071-021-06857-7

Cited by 66 publications

(29 citation statements)

References 31 publications

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“…The average RMS and the average power of the signals in the CWRU and the UoC dataset were 0.27, −9.36 dB and 0.07, −21.91 dB, respectively. Preprocessing methods such as stochastic resonance [50] can be used to enhance weak fault characteristics in datasets such as UoC; however, in this paper, the LWSN method was applied directly to the raw vibration data.…”

Section: Gear Fault Diagnosismentioning

confidence: 99%

Learnable Wavelet Scattering Networks: Applications to Fault Diagnosis of Analog Circuits and Rotating Machinery

2022

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Analog circuits are a critical part of industrial electronics and systems. Estimates in the literature show that, even though analog circuits comprise less than 20% of all circuits, they are responsible for more than 80% of faults. Hence, analog circuit fault diagnosis and isolation can be a valuable means of ensuring the reliability of circuits. This paper introduces a novel technique of learning time–frequency representations, using learnable wavelet scattering networks, for the fault diagnosis of circuits and rotating machinery. Wavelet scattering networks, which are fixed time–frequency representations based on existing wavelets, are modified to be learnable so that they can learn features that are optimal for fault diagnosis. The learnable wavelet scattering networks are developed using the genetic algorithm-based optimization of second-generation wavelet transform operators. The simulation and experimental results for the diagnosis of analog circuit faults demonstrates that the developed diagnosis scheme achieves greater fault diagnosis accuracy than other methods in the literature, even while considering a larger number of fault classes. The performance of the diagnosis scheme on benchmark datasets of bearing faults and gear faults shows that the developed method generalizes well to fault diagnosis in multiple domains and has good transfer learning performance, too.

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Section: Gear Fault Diagnosismentioning

confidence: 99%

Learnable Wavelet Scattering Networks: Applications to Fault Diagnosis of Analog Circuits and Rotating Machinery

2022

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“…The use of GA helps achieve an optimized weight value for BPN to obtain the desired output for the fault situation. Thus, the neuro-genetic technique based on a FPGA when implemented functions in such a way that the control scheme is capable of fast processing to achieve fault diagnosis almost instantly [ 12 , 13 ]. Here, the mixed design of neural networks and genetic algorithms is developed and implemented in the FPGA process as given by the flow diagram in Figure 5 .…”

Section: Neuro-genetic Approach For Fault Classificationmentioning

confidence: 99%

“…The CM system obtains basic data information from the motor via the use of signal processing or data analysis methods as described before. Although the method does not need human interpretation, it does have a fundamental downside [ 11 , 12 , 13 ]. The automation of the fault detection and diagnosis process is a natural evolution in the development of CM technologies [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

FPGA Implementation of AI-Based Inverter IGBT Open Circuit Fault Diagnosis of Induction Motor Drives

et al. 2022

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In modern industrial manufacturing processes, induction motors are broadly utilized as industrial drives. Online condition monitoring and diagnosis of faults that occur inside and/or outside of the Induction Motor Drive (IMD) system make the motor highly reliable, helping to avoid unscheduled downtimes, which cause more revenue loss and disruption of production. This can be achieved only when the irregularities produced because of the faults are sensed at the moment they occur and diagnosed quickly so that suitable actions to protect the equipment can be taken. This requires intelligent control with a high-performance scheme. Hence, a Field Programmable Gate Array (FPGA) based on neuro-genetic implementation with a Back Propagation Neural network (BPN) is suggested in this article to diagnose the fault more efficiently and almost instantly. It is reported that the classification of the neural network will provide the output within 2 µs although the clone procedure with microcontroller requires 7 ms. This intelligent control with a high-performance technique is applied to the IMD fed by a Voltage Source Inverter (VSI) to diagnose the fault. The proposed approach was simulated and experimentally validated.

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“…Bo Chen et al [5] studied the distributed fault diagnosis technology and combined it with software technology, computer network, artificial intelligence and fault diagnosis to improve the self-fault diagnosis function of an expert system. Zijian Qiao et al [6] proposed a second-order stochastic resonance method based on fractional derivative enhancement, which uses strong background noise to enhance the weak fault characteristics. It is used for mechanical fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

A Model for Flywheel Fault Diagnosis Based on Fuzzy Fault Tree Analysis and Belief Rule Base

Cheng¹,

Liu²,

He³

et al. 2022

Machines

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In the fault diagnosis of the flywheel system, the input information of the system is uncertain. This uncertainty is mainly caused by the interference of environmental factors and the limited cognitive ability of experts. The BRB (belief rule base) shows a good ability for dealing with problems of information uncertainty and small sample data. However, the initialization of the BRB relies on expert knowledge, and it is difficult to obtain the accurate knowledge of flywheel faults when constructing BRB models. Therefore, this paper proposes a new BRB model, called the FFBRB (fuzzy fault tree analysis and belief rule base), which can effectively solve the problems existing in the BRB. The FFBRB uses the Bayesian network as a bridge, uses an FFTA (fuzzy fault tree analysis) mechanism to build the BRB’s expert knowledge, uses ER (evidential reasoning) as its reasoning tool, and uses P-CMA-ES (projection covariance matrix adaptation evolutionary strategies) as its optimization model algorithm. The feasibility and superiority of the proposed method are verified by an example of a flywheel friction torque fault tree.

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A second-order stochastic resonance method enhanced by fractional-order derivative for mechanical fault detection

Cited by 66 publications

References 31 publications

Learnable Wavelet Scattering Networks: Applications to Fault Diagnosis of Analog Circuits and Rotating Machinery

Learnable Wavelet Scattering Networks: Applications to Fault Diagnosis of Analog Circuits and Rotating Machinery

FPGA Implementation of AI-Based Inverter IGBT Open Circuit Fault Diagnosis of Induction Motor Drives

A Model for Flywheel Fault Diagnosis Based on Fuzzy Fault Tree Analysis and Belief Rule Base

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