Induction machine fault detection using smartphone recorded audible noise

Vaimann, Toomas; Sobra, Jan; Belahcen, Anouar; Rassõlkin, Anton; Rolak, Michal; Kallaste, Ants

doi:10.1049/iet-smt.2017.0104

Cited by 42 publications

(26 citation statements)

References 27 publications

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“…This provides useful information about the vibration signal at different frequency bands. Considering level-N decomposition for the input vibration signal, the energy of detail components at each decomposition level is defined according to (4) [30]and the energy of approximation components at level N is defined according to (5)…”

Section: Mathematical Backgroundmentioning

confidence: 99%

Machine‐learning approach for fault detection in brushless synchronous generator using vibration signals

Rahnama

Vahedi

Mohammad‐Alikhani

et al. 2019

IET Science, Measurement & Technology

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In order to maintain continuous production and to avoid the maintenance cost increment in power plants, it is important to monitor the condition of equipment, especially the generator. Regarding the impossibility of direct access to rotating diodes in brushless synchronous generators, the condition monitoring of these elements is very important. In this paper, a novel fault detection method is proposed for the diode rectifier of brushless synchronous generator. At the first stage of this method, the vibration signals are recorded and feature extraction is performed by calculating the relative energy of discrete wavelet transform components. Multiclass support vector machine (MSVM) is used for classification, and the best mother wavelet and number of decomposition level are chosen based on classification performance. To enhance the performance of the classification, a modified sequential forward subset selection approach is included by which the best statistical features are selected. In this approach, besides selecting the best subset of statistical features, the classification parameter is tuned according to the selected subset to achieve the best performance. The result of the proposed method is eventually compared with those results of classification performance using conventional subset selection. Experimental results show that the proposed method can detect rectifier faults effectively.

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Section: Mathematical Backgroundmentioning

confidence: 99%

Machine‐learning approach for fault detection in brushless synchronous generator using vibration signals

Rahnama

Vahedi

Mohammad‐Alikhani

et al. 2019

IET Science, Measurement & Technology

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“…In addition, any fault in the power scheme leads to the change of the vibration pattern. For example, an analysis of the vibrations of an induction machine allows for the detection of broken rotor bars [12,13], as well as rotor and stator wire insulation defects [14]. The power scheme may be non-electrical.…”

Section: Typical Use Of Vibrations In Techncial Diagnisticsmentioning

confidence: 99%

Customized Vibration Generator for State of Health Monitoring of Prosthetic Implants and Pseudo-Bionic Machine–Human Feedbacks

et al. 2019

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Modern industrial, household and other equipment include sophisticated power mechanisms and complicated control solutions that require tighter human–machine–human interactions to form the structures known as cyber–physical–human systems. Their significant parts are human–machine command links and machine–human feedbacks. Such systems are found in medicine, e.g., in orthopedics, where they are important for the operation and functional abilities of orthopedic devices—wheelchair, prosthesis, rehabilitation units, etc. The mentioned feedbacks may be implemented based on the haptic perceptions that requires vibration actuators. In orthopedics, such actuators can be used also for diagnostic purposes. This research brings forward the idea of the use of 3D printing in conjunction with high quality permanent magnets. This allows for the achievement of better efficiency, smaller size, and the developing of actuators individually for particular circumstances. The obtained simulation, experimental data, and data about 3D manufacturing generally confirm the above hypothesis. In particular, the stiffness coefficient of the actuator’s membrane and attached mass, which can be changed easily during 3D printing, affects the frequency of maximal power output. Secondly, the 3D manufacturing process is quick, tunable and rather cheap. Finally, an elaboration of the design of the actuator that allows for the real-time modification of stiffness and mass in a program way is planned for future works.

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“…In the field of machine fault diagnostics, the researchers are trying to implement ANN as artificial intelligent technique to get better and more precise results. In [52] the authors use ANN to prove the possibility of fault detection through smartphone recorded sound files. [41] proposed ANN along with wavelet packet decomposition (WPD) for detection of BRB and claimed that this method is better in accuracy, exact measurement of slip is not required, and diagnostics can be performed with reduced load conditions.…”

Section: Advanced Techniquesmentioning

confidence: 99%

Review of Electrical Machine Diagnostic Methods Applicability in the Perspective of Industry 4.0

Asad

Rassõlkin

Kallaste

et al. 2018

Electrical, Control and Communication Engineering

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Digitalization of the industrial sector and Industry 4.0 have opened new horizons in many technical fields, including electrical machine diagnostics and operation, as well as machine condition monitoring. This paper addresses a selection of electrical machine diagnostics methods that are applicable for the use in the perspective of Industry 4.0, to be used in hand with cloud environments and the possibilities granted by the Internet of Things. The need for further research and development in the field is pointed out. Some potentially applicable future approaches are presented.

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Induction machine fault detection using smartphone recorded audible noise

Cited by 42 publications

References 27 publications

Machine‐learning approach for fault detection in brushless synchronous generator using vibration signals

Machine‐learning approach for fault detection in brushless synchronous generator using vibration signals

Customized Vibration Generator for State of Health Monitoring of Prosthetic Implants and Pseudo-Bionic Machine–Human Feedbacks

Review of Electrical Machine Diagnostic Methods Applicability in the Perspective of Industry 4.0

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