A signal processing approach to bearing fault detection with the use of a mobile phone

Rzeszucinski, Pawel; Orman, Maciej; Pinto, Cajetan T.; Tkaczyk, Agnieszka; Sułowicz, Maciej

doi:10.1109/demped.2015.7303707

Cited by 16 publications

(11 citation statements)

References 14 publications

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“…Reasons include availability of new sources through sub-system inclusion,utilisation of actuators as sensors (such as shared use of data for motion control and condition monitoring),exploitation of new data source concepts (e.g., utilisation of consumer-market smartphones for vibration measurements [10]).…”

Section: Related Workmentioning

confidence: 99%

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An Approach to Automated Fusion System Design and Adaptation

Fritze

Mönks

Holst

et al. 2017

Sensors

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Industrial applications are in transition towards modular and flexible architectures that are capable of self-configuration and -optimisation. This is due to the demand of mass customisation and the increasing complexity of industrial systems. The conversion to modular systems is related to challenges in all disciplines. Consequently, diverse tasks such as information processing, extensive networking, or system monitoring using sensor and information fusion systems need to be reconsidered. The focus of this contribution is on distributed sensor and information fusion systems for system monitoring, which must reflect the increasing flexibility of fusion systems. This contribution thus proposes an approach, which relies on a network of self-descriptive intelligent sensor nodes, for the automatic design and update of sensor and information fusion systems. This article encompasses the fusion system configuration and adaptation as well as communication aspects. Manual interaction with the flexibly changing system is reduced to a minimum.

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Section: Related Workmentioning

confidence: 99%

“…exploitation of new data source concepts (e.g., utilisation of consumer-market smartphones for vibration measurements [10]).…”

Section: Related Workmentioning

confidence: 99%

An Approach to Automated Fusion System Design and Adaptation

Fritze

Mönks

Holst

et al. 2017

Sensors

View full text Add to dashboard Cite

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“…So far, not too many research papers have been dedicated to electrical machine diagnostics through the medium of mobile phones. One of the latest and experimentally verified papers is [20], where only the detection of bearing faults with the help of a mobile phone is investigated.…”

Section: Introductionmentioning

confidence: 99%

Induction machine fault detection using smartphone recorded audible noise

Vaimann

Sobra

Belahcen

et al. 2018

IET Science, Measurement & Technology

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This paper presents induction machine fault detection possibilities using smartphone recorded audible noise. Different faults of the induction machine, such as various numbers of broken rotor bars and rotor eccentricities are inflicted to the machine. Analysis is performed on audible noise recorded by two different smartphones and compared with mechanical vibrations recorded by sensors. Neural network is composed and probabilities of fault detection using such diagnostic measures are presented. Necessity for further study is pointed out.

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“…Generally, when a fault occurs in a rolling element, it is accompanied by a certain amount of vibration and sound. Thus, potential faults can be well detected with appropriate techniques for processing the collected vibration or acoustic signals [ 5 , 6 ]. Due to the fact that vibration signals carry rich information about potential faults, vibration analysis has been widely applied to diagnose the faults of rolling element bearings, which always includes two procedures: one is the feature extraction of vibration signals with signal processing techniques, and the other is the fault pattern recognition for the extracted features [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Fault Diagnosis Approach for Rotating Machinery with the Fusion of Entropy-Based Feature Extraction and SVM Optimized by a Chaos Quantum Sine Cosine Algorithm

Tan

et al. 2018

Entropy

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As crucial equipment during industrial manufacture, the health status of rotating machinery affects the production efficiency and device safety. Hence, it is of great significance to diagnose rotating machinery faults, which can contribute to guarantee the running stability and plan for maintenance, thus promoting production efficiency and economic benefits. For this purpose, a hybrid fault diagnosis model with entropy-based feature extraction and SVM optimized by a chaos quantum sine cosine algorithm (CQSCA) is developed in this research. Firstly, the state-of-the-art variational mode decomposition (VMD) is utilized to decompose the vibration signals into sets of components, during which process the preset parameter K is confirmed with the central frequency observation method. Subsequently, the permutation entropy values of all components are computed to constitute the feature vectors corresponding to different kind of signals. Later, the newly developed sine cosine algorithm (SCA) is employed and improved with chaotic initialization by a Duffing system and quantum technique to optimize the support vector machine (SVM) model, with which the fault pattern is recognized. Additionally, the availability of the optimized SVM with CQSCA was revealed in pattern recognition experiments. Finally, the proposed hybrid fault diagnosis approach was employed for engineering applications as well as contrastive analysis. The comparative results show that the proposed method achieved the best training accuracy 99.5% and best testing accuracy 97.89%. Furthermore, it can be concluded from the boxplots of different diagnosis methods that the stability and precision of the proposed method is superior to those of others.

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A signal processing approach to bearing fault detection with the use of a mobile phone

Cited by 16 publications

References 14 publications

An Approach to Automated Fusion System Design and Adaptation

An Approach to Automated Fusion System Design and Adaptation

Induction machine fault detection using smartphone recorded audible noise

A Hybrid Fault Diagnosis Approach for Rotating Machinery with the Fusion of Entropy-Based Feature Extraction and SVM Optimized by a Chaos Quantum Sine Cosine Algorithm

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