Detection of Shaft Misalignment Using Piezoelectric Strain Sensors

Gama, Antonio Lopes; Lima, Wagner Costa; Veneza, Joao P. S. de

doi:10.1007/s40799-016-0158-x

Cited by 14 publications

(9 citation statements)

References 5 publications

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“…28 This MFS was used to detect parallel and angular misalignment with rigid coupling for 20 and 3 Hz rotational speed by analysing the spectra of vibrations and PSS. 22 The vibration spectrum analysis showed different behaviour of the two types of crack, and it found early detection threshold for both the cracks. 26 The experiments were performed on MFS for angular and parallel misalignments and breathing crack by obtaining vibration data on different speeds to study sensitive location for transducer.…”

Section: Order Analysismentioning

confidence: 98%

“…PSS showed high detection sensitivity in the case of the misaligned shaft and low detection sensitivity for unbalance. 22 Experimental vibration data were collected from a rig that consists of a shaft that rotates under its own weight at the steady state for different crack conditions. Nine different crack levels were induced and signals were processed by combining the wavelet packet transform energy and ANN (radial basis function-artificial neural network (RBF-ANN)).…”

Section: Introductionmentioning

confidence: 99%

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Experimental condition monitoring for the detection of misaligned and cracked shafts by order analysis

Azeem

Yuan

Raza

et al. 2019

Advances in Mechanical Engineering

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Traditional industry is shifting towards the 'industry 4.0 factory' that incorporates automatic fault detection and correction. Industry 4.0 also includes online condition monitoring to make maintenance decisions on the basis of the health of a single machine. This research article presents order analysis for detecting two common problems in rotating machinery-misalignment and cracks. The systematic and detailed experimentations were performed on SpectraQuest's Machinery Fault Simulatorä, and the time domain data acquired through the accelerometers mounted on motor and rotor inboard and outboard bearing housing were transformed into spectra using a fast Fourier transform. Two sets of experiments have been performed for misalignment and one set for shaft cracks. Three levels of misalignment with two types of loading conditions have been analysed. The faulty vibration data were compared with the healthy shaft. The misaligned shaft shows higher vibration amplitude at 2 3 running speed and harmonic vibration behaviour. The slit repair and V-notch crack shaft models were used to analyse the effect of cracks on vibrations and the resulting vibration spectra showed peaks at 2 3 and 3 3 running speed. These results indicate that order analysis is helpful in detecting misaligned and cracked shafts, supporting industry 4.0 by facilitating the automatic detection of faults.

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Section: Order Analysismentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Experimental condition monitoring for the detection of misaligned and cracked shafts by order analysis

Azeem

Yuan

Raza

et al. 2019

Advances in Mechanical Engineering

View full text Add to dashboard Cite

show abstract

“…The authors concluded that imbalance and misalignment can be characterized by one and twice the speed of operation in the corresponding spectra. Reddy and Sekhar [25] developed a method using strain sensors for the detection of shaft misalignment at low speed. Wang et al [26] used the torque signal for the detection of coupling misalignment in the rotor.…”

Section: Related Workmentioning

confidence: 99%

Faults Detection and Classification under Variable Condition Using Intrinsic Time - Scale Decomposition and Neural Network

Leddine¹,

Chamceddine²,

Zenasni³

2021

JESA

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Misalignment and unbalance are a common fault occurring in the rotor system. A new approach for detecting misalignment and unbalance problems combining the intrinsic time - scale decomposition (ITD), the root mean square (RMS) and perceptron multilayer network (MLP) is proposed in this paper. Vibration signals of normal condition, misalignment horizontal, misalignment vertical and unbalance with different level are collected under different speed. ITD, nonlinear analysis of signals, was applied to decompose the vibration signals into 8 proper rotation components. The RMS values of 8 components are calculated and using as features vector. Last, the perceptron multilayer network was used for fault identification and classification. The proposed approach accurately classified and detection of unbalance and misalignment; the average accuracy achieved is 97.99%.

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“…36,37 In modern industrial applications, 38,39 rotating machinery (RM) [40][41][42] Introduction of CM and FD of IM based on AE Importantly, CM and FD based on the AE of RM have been growing over recent years. AE is widely used in rotating electric motors CM and FD [186][187][188] for both electrical and mechanical faults including, the shaft misalignment, 189 the bearing, [190][191][192] the rotor 193,194 and the stator. 195 In addition, AE is extensively used in CM and FD for the gearbox.…”

Section: Introductionmentioning

confidence: 99%

Sounds and acoustic emission-based early fault diagnosis of induction motor: A review study

AlShorman

Alkahatni

Masadeh

et al. 2021

Advances in Mechanical Engineering

131

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Nowadays, condition-based maintenance (CBM) and fault diagnosis (FD) of rotating machinery (RM) has a vital role in the modern industrial world. However, the remaining useful life (RUL) of machinery is crucial for continuous monitoring and timely maintenance. Moreover, reduced maintenance costs, enhanced safety, efficiency, reliability, and availability are the main important industrial issues to maintain valuable and high-cost machinery. Undoubtedly, induction motor (IM) is considered to be a pivotal component in industrial machines. Recently, acoustic emission (AE) becomes a very accurate and efficient method for fault, leaks and fatigue detection and monitoring techniques. Moreover, CM and FD based on the AE of IM have been growing over recent years. The proposed research study aims to review condition monitoring (CM) and fault diagnosis (FD) studies based on sound and AE for four types of faults: bearings, rotor, stator, and compound. The study also points out the advantages and limitations of using sound and AE analysis in CM and FD. Existing public datasets for AE based analysis for CM and FD of IM are also mentioned. Finally, challenges facing AE based CM and FD for RM, especially for IM, and possible future works are addressed in this study.

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Detection of Shaft Misalignment Using Piezoelectric Strain Sensors

Cited by 14 publications

References 5 publications

Experimental condition monitoring for the detection of misaligned and cracked shafts by order analysis

Experimental condition monitoring for the detection of misaligned and cracked shafts by order analysis

Faults Detection and Classification under Variable Condition Using Intrinsic Time - Scale Decomposition and Neural Network

Sounds and acoustic emission-based early fault diagnosis of induction motor: A review study

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