Experimental study on the Condition Monitoring of Shaft Unbalance by using Vibrations Spectrum and phase Analysis

Azeem, Naqash; Yuan, Xiao

doi:10.1109/cmd.2018.8535737

Cited by 10 publications

(5 citation statements)

References 17 publications

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“…Figure 4 shows the unbalanced mass attached to the disc mounted at the center of the shaft. The crack initiated resembles the RCF in the bearing interior parts, which is a prominent failure in rotating machines [4]. In this research study, 16 fault classes with various combinations of good shaft, good bearing, good gear, unbalanced shaft, inner race bearing fault, outer race bearing fault, inner and outer race fault and gear fault are considered and the experiments are conducted at 600, 700, 800 and 900 rpm.…”

Section: Methodsmentioning

confidence: 99%

“…The most frequent flaw in rotating machinery is imbalance, which is caused by an uneven distribution of mass around the shaft's center line. In contrast to static loading, the force created by unbalance is varying because of the shaft's rotation, which can quickly lead to failure [4]. In industrial machinery, rolling element bearings are frequently employed to support shaft load and enable relative motion.…”

Section: Introductionmentioning

confidence: 99%

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Enhanced quadratic discriminant analysis with sensor signal fusion for speed-independent fault detection in rotating machines

et al. 2023

Meas. Sci. Technol.

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The predictive maintenance approach in the fault diagnosis of rotating machines is becoming important in industries in order to minimize operational cost and to improve the reliability of machines. Recent studies have focused on developing an effective prediction model to detect machinery faults at various speeds using machine learning techniques. In this research study, machine-learning-based models are developed to detect multi-component faults such as gear faults, bearing faults, and unbalanced shafts, by training the four speed data sets using decision trees, support vector machines and artificial neural networks. The machine learning models are improvised by fusing the vibration signals (X, Y and Z) and sound signals with the feature selection algorithm, minimal redundancy-maximum relevance (mRMR). The research work is extended by training a model with any three speeds and testing the trained model with the remaining speed. The state-of-the-art algorithms used to train all four speeds yield poor performance in the prediction of faults at testing speeds. A linear and quadratic discriminant analysis (QDA) is chosen based on its multivariate discrimination capability for better fault prediction at testing speed. The proposed combination of QDA with mRMR selection of fused vibration and sound feature set perform well in the detection of multiple faults at variable speeds.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhanced quadratic discriminant analysis with sensor signal fusion for speed-independent fault detection in rotating machines

et al. 2023

Meas. Sci. Technol.

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“…All of the above defected bearings in this experimental study were used at 1002, 1500, 2400, and 3000 RPM rotating speeds, with two types of loading conditions: without in- This simulator was used in our previous studies to detect: the imbalance caused by using accelerometers at three different operating speeds [25], the misaligned and cracked shafts using order analysis [26], the bearing faults using spectral density analysis [27] and octave analysis [28]. It was also used to detect imbalance using piezoelectric strain sensors [29].…”

Section: Methodsmentioning

confidence: 99%

“…The overall velocity-RMS values have been collected at no fault condition for both types of loading conditions in order to follow the ISO standard 20816-1 [31]; this was also done in our previous studies [25][26][27][28]. The ISO standard 20816-1 has a vibration severity chart consisting of four categories: A, B, C, and D. Category 'A' means that the machine is in outstanding condition, and category 'D' means that the machine's vibrations are not permissible.…”

Section: Methodsmentioning

confidence: 99%

Vibration Energy at Damage-Based Statistical Approach to Detect Multiple Damages in Roller Bearings

et al. 2022

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This study proposes a statistical approach based on vibration energy at damage to detect multiple damages occurring in roller bearings. The analysis was performed at four different rotating speeds—1002, 1500, 2400, and 3000 RPM—following four different damages—inner race, outer race, ball, and combination damage—and under two types of loading conditions. These experiments were performed on a SpectraQuest Machinery Fault Simulator™ by acquiring the vibration data through accelerometers under two operating conditions: with the bearing loader on the rotor shaft and without the bearing loader on the rotor shaft. The histograms showed diversity in the defected bearing as compared to the intact bearing. There was a marked increase in the kurtosis values of each damaged roller bearing. This research article proposes that histograms, along with kurtosis values, represent changes in vibration energy at damage that can easily detect a damaged bearing. This study concluded that the vibration energy at damage-based statistical technique is an outstanding approach to detect damages in roller bearings, assisting Industry 4.0 to diagnose faults automatically.

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“…Desde el punto de vista del análisis del comportamiento vibratorio, en la dirección radial, se aprecia un aumento en la amplitud de vibración en el primer armónico de la velocidad de rotación en el espectro de los datos y una forma de onda sinusoidal en el dominio del tiempo [46]. Numerosos estudios como [47] o [48] concluyen que cuando hay un desequilibrio, en el espectro se observa un aumento de la amplitud en el primer armónico (1x), es decir, a la frecuencia de rotación del eje.…”

Section: Detección De Desequilibrio En Ejesunclassified

Analysis in the time-frequency domain of different depths of a crack located in a change of section of a shaft

Zamorano

Gómez

Castejón

et al. 2019

Advances in Mechanism and Machine Science

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Experimental study on the Condition Monitoring of Shaft Unbalance by using Vibrations Spectrum and phase Analysis

Cited by 10 publications

References 17 publications

Enhanced quadratic discriminant analysis with sensor signal fusion for speed-independent fault detection in rotating machines

Enhanced quadratic discriminant analysis with sensor signal fusion for speed-independent fault detection in rotating machines

Vibration Energy at Damage-Based Statistical Approach to Detect Multiple Damages in Roller Bearings

Analysis in the time-frequency domain of different depths of a crack located in a change of section of a shaft

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