Fault Diagnosis System of Rotating Machinery Vibration Signal

You, Lei; Hu, Jun; Fang, Fang; Duan, Lin-Tao

doi:10.1016/j.proeng.2011.08.125

Cited by 10 publications

(7 citation statements)

References 3 publications

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“…Indeed, the proposed methodology has been designed for being adapted to different and/or multiple available physical magnitudes. In this regard, although the analysis of vibration signals represents the most significant source of information when the bearing fault diagnosis is addressed and faced [ 1 , 12 ], the proposed scheme allows the consideration of additional sources of information, that is, multiple vibration axis or even complementary physical magnitudes as the stator currents. Therefore, as Figure 3 depicts, the proposed approach may support a number of N available signals where the considered index i , for I = 1, 2,…, N , is used to identify each one of the considered physical magnitudes.…”

Section: Deep Feature Learning Based Methodologymentioning

confidence: 99%

“…In this regard, the consideration of advanced materials, such as hybrid or ceramic bearings, are of high interest in the move towards high-performance and oil-free rotatory electromechanical actuators. High demanding operative requirements have become the main characteristics of modern rotating machinery as well as their continuous operation, which also increases the risk of system failure and, specifically, the acceleration of bearing faults as one of the most common sources of malfunction [ 1 , 2 ].…”

Section: Introductionmentioning

confidence: 99%

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Diagnosis Methodology Based on Deep Feature Learning for Fault Identification in Metallic, Hybrid and Ceramic Bearings

Saucedo-Dorantes

Arellano-Espitia

Delgado-Prieto

et al. 2021

Sensors

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Scientific and technological advances in the field of rotatory electrical machinery are leading to an increased efficiency in those processes and systems in which they are involved. In addition, the consideration of advanced materials, such as hybrid or ceramic bearings, are of high interest towards high-performance rotary electromechanical actuators. Therefore, most of the diagnosis approaches for bearing fault detection are highly dependent of the bearing technology, commonly focused on the metallic bearings. Although the mechanical principles remain as the basis to analyze the characteristic patterns and effects related to the fault appearance, the quantitative response of the vibration pattern considering different bearing technology varies. In this regard, in this work a novel data-driven diagnosis methodology is proposed based on deep feature learning applied to the diagnosis and identification of bearing faults for different bearing technologies, such as metallic, hybrid and ceramic bearings, in electromechanical systems. The proposed methodology consists of three main stages: first, a deep learning-based model, supported by stacked autoencoder structures, is designed with the ability of self-adapting to the extraction of characteristic fault-related features from different signals that are processed in different domains. Second, in a feature fusion stage, information from different domains is integrated to increase the posterior discrimination capabilities during the condition assessment. Third, the bearing assessment is achieved by a simple softmax layer to compute the final classification results. The achieved results show that the proposed diagnosis methodology based on deep feature learning can be effectively applied to the diagnosis and identification of bearing faults for different bearing technologies, such as metallic, hybrid and ceramic bearings, in electromechanical systems. The proposed methodology is validated in front of two different electromechanical systems and the obtained results validate the adaptability and performance of the proposed approach to be considered as a part of the condition-monitoring strategies where different bearing technologies are involved.

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Section: Deep Feature Learning Based Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Diagnosis Methodology Based on Deep Feature Learning for Fault Identification in Metallic, Hybrid and Ceramic Bearings

Saucedo-Dorantes

Arellano-Espitia

Delgado-Prieto

et al. 2021

Sensors

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“…Lei You et al (2011) presented a new fault diagnosis system of rotating machinery vibration signal to improve the precision of testing vibration signal. They used an advanced PXI test platform, adopting 16 bits resolution A/D device and FPGA technology in the design.…”

Section: Introductionmentioning

confidence: 99%

Vibration Signature of Normal and Notched Tooth Gear Pump

Osman

Gobran

Mahmoud

2019

ESJ

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Gear pumps are critical devices in many industrial applications. An unexpected failure of the gear pump may cause significant effect on its performance. Consequently, there will be economic losses. As a result of that, fault diagnosis in gears has been the subject of intensive research. Vibration analysis has been used as an effective tool in machines diagnosis and in machinery maintenance decisions. As a rule, an increased vibration level is a warning form before failure or breakdown. By measuring and analyzing the gear pump vibration, it is possible to determine both the nature and severity of the defect, and hence predict the machine's failure. The vibration signal of a gear pump carries the signature of the fault in the gears, and early fault detection of the gear pump is possible by analyzing the vibration signal using different signal processing techniques. This paper presents, experimentally, the external gear pump signature for normal and faulty gear pumps at different rotational speeds (1080, 1200, and 1439rpm). The considered faults herein are two different notches on one of the pump teeth-small notch and large notch. The paper concludes that features of the vibration are different with the notch shape and the rotational speed. The amplitude of vibration increases by increasing both rotational speed and notch size.

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“…An example of the MEMS would be the application of vibration-based ICs used for the diagnosis of equipment using a vibrational signal via a remote monitoring system. 9 In order to extract more energy from environmental vibrational energy, many energy harvesters have been proposed. Also, various piezoelectric energy harvesters with low electrical power have been created.…”

Section: Introductionmentioning

confidence: 99%

“…An example of the MEMS would be the application of vibration-based ICs used for the diagnosis of equipment using a vibrational signal via a remote monitoring system. 9…”

Section: Introductionmentioning

confidence: 99%

Optimization of a two-mass vibration-based electromagnetic energy harvester using simulated annealing method

Chiu

Chang

Yeh

et al. 2018

Journal of Low Frequency Noise, Vibration and Active Control

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In this paper, a theoretical mathematical model in conjunction with an electrical generation model is examined. Using a simulated algorithm, the optimal design of a two-mass energy harvester that finds the maximal electrical power will be assessed. Before the optimal design is performed, the influence of the electrical power with respect to design parameters such as the magnet's height, the diameter, the stiffness of the lower springs, the stiffness of the upper springs, the revolution of the lower coil, the revolution of the upper coil, the diameter of the coil's wire, and the electrical resistance of the loading will be analyzed. Results reveal that the design parameters play essential roles in maximizing electrical power. The two mode shapes of the two-mass energy harvester also occur at the targeted forcing frequencies. The electrical power is optimally extracted at the two primary forcing frequencies, i.e. 12 and 30 Hz. Moreover, it is obvious that the induced electrical power of the two-mass energy harvester will be superior to that of the one-mass energy harvester.

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Fault Diagnosis System of Rotating Machinery Vibration Signal

Cited by 10 publications

References 3 publications

Diagnosis Methodology Based on Deep Feature Learning for Fault Identification in Metallic, Hybrid and Ceramic Bearings

Diagnosis Methodology Based on Deep Feature Learning for Fault Identification in Metallic, Hybrid and Ceramic Bearings

Vibration Signature of Normal and Notched Tooth Gear Pump

Optimization of a two-mass vibration-based electromagnetic energy harvester using simulated annealing method

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