Improved Structural Rotor Fault Diagnosis Using Multi-Sensor Fuzzy Recurrence Plots and Classifier Fusion

Nath, Aneesh G; Udmale, Sandeep S.; Raghuwanshi, Divyanshu; Singh, Sanjay Kumar

doi:10.1109/jsen.2021.3079366

Cited by 32 publications

(14 citation statements)

References 46 publications

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“…Additionally, the division of the datasets into training, test and validation data differs significantly between the individual works. Many studies conduct a random train-test split between all the available data [3,22,27] as this is a common practice in the machine learning field. However, as stated above, vibration characteristics vary only slightly once a setup is completely assembled.…”

Section: Discussion and Outlookmentioning

confidence: 99%

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Condition Monitoring of Drive Trains by Data Fusion of Acoustic Emission and Vibration Sensors

et al. 2021

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Early damage detection and classification by condition monitoring systems is crucial to enable predictive maintenance of manufacturing systems and industrial facilities. Data analysis can be improved by applying machine learning algorithms and fusion of data from heterogenous sensors. This paper presents an approach for a step-wise integration of classifications gained from vibration and acoustic emission sensors in order to combine the information from signals acquired in the low and high frequency ranges. A test rig comprising a drive train and bearings with small artificial damages is used for acquisition of experimental data. The results indicate that an improvement of damage classification can be obtained using the proposed algorithm of combining classifiers for vibrations and acoustic emissions.

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Section: Discussion and Outlookmentioning

confidence: 99%

“…ML has been successfully applied in a previous work to this paper for the detection of mechanical faults such as imbalances [1]. Several works investigated how data from multiple vibration sensors can be jointly processed to obtain diagnostic information about a rotating machine [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Condition Monitoring of Drive Trains by Data Fusion of Acoustic Emission and Vibration Sensors

et al. 2021

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“…With the help of Internet of Things technology, multiple sensors are installed on circuit breakers [7] to monitor the fault status of circuit breaker springs and collect fault status signals. This signal has the characteristics of nonlinearity, low signal energy, and being easily submerged in noise, which is not conducive to monitoring the degree of circuit breaker spring failure.…”

Section: Feature Extraction Of 1 Circuit Breaker Spring Fault Statementioning

confidence: 99%

Design of fault degree diagnosis algorithm for circuit breaker spring based on fuzzy clustering

Zhang,

Ying,

Wang

et al. 2024

J. Phys.: Conf. Ser.

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This paper proposes a design of a fault diagnosis algorithm for circuit breaker springs based on fuzzy clustering. The features of the fault state signal are extracted by combining the methods of Intrinsic Time-Scale Decomposition and Singular Spectrum Analysis. Using the fuzzy clustering method, this study classifies circuit breaker spring faults, extracts fault features, and achieves fault degree diagnosis. The experimental results show that the algorithm has high accuracy in fault diagnosis.

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“…RPs operate by first performing a cluster analysis on the data and then building graphical patterns to illustrate recurrences in the data. RPs have been used in many applications for example fault diagnosis of machinery [7] and in image transformations [8].…”

Section: Introductionmentioning

confidence: 99%

Periodicity Intensity Reveals Insights into Time Series Data: Three Use Cases

Smeaton,

2023

Preprint

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Improved Structural Rotor Fault Diagnosis Using Multi-Sensor Fuzzy Recurrence Plots and Classifier Fusion

Cited by 32 publications

References 46 publications

Condition Monitoring of Drive Trains by Data Fusion of Acoustic Emission and Vibration Sensors

Condition Monitoring of Drive Trains by Data Fusion of Acoustic Emission and Vibration Sensors

Design of fault degree diagnosis algorithm for circuit breaker spring based on fuzzy clustering

Periodicity Intensity Reveals Insights into Time Series Data: Three Use Cases

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