Formal Language Generation for Fault Diagnosis With Spectral Logic via Adversarial Training

Chen, Gang; Peng, Wei; Jiang, Huiming; Liu, Mei

doi:10.1109/tii.2020.3040743

Cited by 11 publications

(7 citation statements)

References 27 publications

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“…The formulas can be seen as the interpretation of the faults. For example, for the inner race fault, the formula ϕ I can be read "eventually within [0, 5], always within [58, 66], the signals should be larger than or equal to 0.05, and always within [14,31], the signals should be smaller than 0.3, and always within [45, 52], the signals should be smaller than 0.04." The other faults can be interpreted in the same way.…”

Section: Case Studiesmentioning

confidence: 99%

“…Moreover, such interpretations are mostly based on only one layer feature of the DL, which cannot provide an explanation about how the decision is made formally. To address these issue, interpretable fault diagnosis based on formal languages has attracted much attention in the field of monitoring [13], [14]. Formal language-based fault diagnosis methods try to use a neural network to learn a formal language, e.g., temporal logic [15] and spectral temporal logic [13], to describe the fault behaviours among the signals.…”

Section: Introductionmentioning

confidence: 99%

“…Fault diagnosis with formal language has been widely used in monitoring tasks [14], [16] in the past decade, which has achieved great success. However, there are many challenges to apply formal language to interpretable fault diagnosis tasks for existing methodologies.…”

Section: Introductionmentioning

confidence: 99%

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Interpretable Fault Diagnosis of Rolling Element Bearings with Temporal Logic Neural Network

Chen¹,

Lu²,

Shi³

et al. 2022

Preprint

Self Cite

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Machine learning-based methods have achieved successful applications in machinery fault diagnosis. However, the main limitation that exists for these methods is that they operate as a black box and are generally not interpretable. This paper proposes a novel neural network structure, called temporal logic neural network (TLNN), in which the neurons of the network are logic propositions. More importantly, the network can be described and interpreted as a weighted signal temporal logic. TLNN not only keeps the nice properties of traditional neuron networks but also provides a formal interpretation of itself with formal language. Experiments with real datasets show the proposed neural network can obtain highly accurate fault diagnosis results with good computation efficiency. Additionally, the embedded formal language of the neuron network can provide explanations about the decision process, thus achieve interpretable fault diagnosis.

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Section: Case Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Interpretable Fault Diagnosis of Rolling Element Bearings with Temporal Logic Neural Network

Chen¹,

Lu²,

Shi³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Li et al [58] proposed an enhanced FBE (EFBE) adopting WPT as the filter of FBE to overcome the shortcomings of the original FBE. Chen et al [59] proposed a novel method to find formal languages, written with signal spectral logic (SSL), to describe the fault behaviors among frequency domain for fault diagnosis. The other some methods are also proposed to realize the fault diagnosis of rolling bearings [60][61][62].…”

Section: Introductionmentioning

confidence: 99%

Rolling Element Fault Diagnosis Based on VMD and Sensitivity MCKD

Cui

Guan

Chen³

2021

IEEE Access

126

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In order to improve the diagnosis accuracy and solve the weak fault signal of rolling element of rolling bearings due to long transmission path, a novel fault diagnosis method based on variational mode decomposition (VMD) and maximum correlation kurtosis deconvolution (MCKD), namely VMD-MCKD-FD is proposed for rolling elements of rolling bearings in this paper. In the proposed VMD-MCKD-FD, the vibration signal of rolling element of rolling bearings is decomposed into a series of Intrinsic Mode Functions (IMFs) by using VMD method. Then the number of modes with outstanding fault information is determined by Kurtosis criterion in order to calculate the deconvolution period T. The periodic fault component of reconstructed signal is enhanced by using sensitivity MCKD method. Finally, the power spectrum of the reconstructed signal is analyzed in detail in order to obtain the fault frequency and diagnose the rolling element fault of rolling bearings. The simulation signal and actual vibration signal are selected to verify the effectiveness of the VMD-MCKD-FD method. The experimental results show that the VMD-MCKD-FD method can effectively diagnose the rolling element fault of rolling bearings and obtain better fault accuracy.

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“…However, the monitoring equipment in ordinary universities is old, the campus security construction is lax, and the limited security management coverage is small. e hardware problems of the monitoring equipment make it difficult for most of the intelligent monitoring systems to cover the whole campus in ordinary universities where the hardware conditions are mixed and far from meeting the level of modern campus security construction [2]. erefore, this paper designs a widely applicable system for the detection and analysis of abnormal student behaviour on campus, which analyses and detects the video stored in the surveillance equipment without the need to update old surveillance equipment, promoting the development of intelligent surveillance and intelligent management in the campus environment.…”

Section: Introductionmentioning

confidence: 99%

A Gaussian High-Dimensional Random Matrix-Based Method for Detecting Abnormal Student Behaviour in Chinese Language Classrooms

Zhang

2022

Mathematical Problems in Engineering

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In this paper, a Gaussian high-dimensional random matrix approach is used to conduct an in-depth study and analysis of the detection of abnormal student behaviour in Chinese language classrooms, and a corresponding model is designed for practical application. The Gaussian high-dimensional random matrix technique and recurrent neural network technique are applied to build a basic technical framework for intelligent proctoring. An innovative method of human posture estimation is used to complete the proctoring task, and the structure of the Gaussian high-dimensional random matrix method is simplified and the model is compressed to achieve real-time and parallelism of the method. A PolSAR sparse representation classification (OGRM_SRC) algorithm based on orthogonal Gaussian random matrices (OGRM) is proposed to construct an observation dictionary based on polarisation features and OGRM to select typical ground target samples in polarized feature images. The system not only functions as a psychometric assessment of students but also is capable of detecting and analysing abnormal student behaviour based on the improved forest algorithm. The residuals of the pixel to be classified are calculated relative to each atom in the observation dictionary, and the minimum reconstruction residual is used as the classification criterion. The OGRM_SRC algorithm is applied to classify the pixel, and the final classification results are obtained and evaluated for accuracy. We propose a fine-grained action recognition optimisation method for recurrent neural networks, fusing temporal attention information and spatial attention information to improve the action recognition method and improve the model’s ability to recognise fine-grained abnormal behaviours such as looking left, looking right, and copying with head down. Based on this, we conducted some comparative experiments to validate the effectiveness of our work and verified that the system achieved a recognition efficiency of 76.8 FPS and a recognition accuracy of 98.5% in a graphical computing processor.

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Formal Language Generation for Fault Diagnosis With Spectral Logic via Adversarial Training

Cited by 11 publications

References 27 publications

Interpretable Fault Diagnosis of Rolling Element Bearings with Temporal Logic Neural Network

Interpretable Fault Diagnosis of Rolling Element Bearings with Temporal Logic Neural Network

Rolling Element Fault Diagnosis Based on VMD and Sensitivity MCKD

A Gaussian High-Dimensional Random Matrix-Based Method for Detecting Abnormal Student Behaviour in Chinese Language Classrooms

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