Diagnosis of Localized Faults in Multistage Gearboxes: A Vibrational Approach by Means of Automatic EMD-Based Algorithm

Buzzoni, Marco; Mucchi, Emiliano; D’Elia, Gianluca; Dalpiaz, Giorgio

doi:10.1155/2017/8345704

Cited by 16 publications

(8 citation statements)

References 35 publications

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“…The Hilbert-Huang transform (HHT) was introduced as a better methodology for analyzing non-linear and non-stationary signals [23]. This technique is now often used for rotational-machine fault diagnosis [24][25][26][27]. The HHT method uses a time adaptive operation known as empirical mode decomposition (EMD) to decompose the signal into a group of complete and orthogonal components, denoted as intrinsic mode functions (IMFs), that represent the intrinsic oscillation modes of the fault-related components of a vibration signal.…”

Section: Introductionmentioning

confidence: 99%

A Reliable Fault Diagnosis Method for a Gearbox System with Varying Rotational Speeds

Nguyen

Prosvirin

Kim

2020

Sensors

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The vibration signals of gearbox gear fault signatures are informative components that can be used for gearbox fault diagnosis and early fault detection. However, the vibration signals are normally non-linear and non-stationary, and they contain background noise caused by data acquisition systems and the interference of other machine elements. Especially in conditions with varying rotational speeds, the informative components are blended with complex, unwanted components inside the vibration signal. Thus, to use the informative components from a vibration signal for gearbox fault diagnosis, the noise needs to be properly distilled from the informational signal as much as possible before analysis. This paper proposes a novel gearbox fault diagnosis method based on an adaptive noise reducer–based Gaussian reference signal (ANR-GRS) technique that can significantly reduce noise and improve classification from a one-against-one, multiclass support vector machine (OAOMCSVM) for the fault types of a gearbox. The ANR-GRS processes the shaft rotation speed to access and remove noise components in the narrowbands between two consecutive sideband frequencies along the frequency spectrum of a vibration signal, enabling the removal of enormous noise components with minimal distortion to the informative signal. The optimal output signal from the ANR-GRS is then extracted into many signal feature vectors to generate a qualified classification dataset. Finally, the OAOMCSVM classifies the health states of an experimental gearbox using the dataset of extracted features. The signal processing and classification paths are generated using the experimental testbed. The results indicate that the proposed method is reliable for fault diagnosis in a varying rotational speed gearbox system.

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Section: Introductionmentioning

confidence: 99%

A Reliable Fault Diagnosis Method for a Gearbox System with Varying Rotational Speeds

Nguyen

Prosvirin

Kim

2020

Sensors

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“…To overcome this drawback, Wu and Huang proposed the ensemble empirical mode decomposition (EEMD) method, which is a noise-assisted data analysis method by adding finite white noise to the investigated signal [ 14 ]. Because of their ease of use and excellent performance for complex signals, EMD-based methods have been widely applied in fault diagnosis [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ]. For example, Lei et al applied the EEMD to the rub-impact fault diagnosis of a power generator and early rub-impact fault diagnosis of a heavy oil catalytic cracking machine [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

“…Park et al applied EEMD to the transmission error measured by the encoders of the input and output shafts to classify the spall and crack faults of gear teeth [ 21 ]. Buzzoni et al applied EMD-based methods for the diagnosis of localized faults in multistage gearboxes, and they pointed out that EMD-based methods were particularly suitable for industrial applications since they were completely automatic [ 22 ]. Considering the nonlinearity of the signal in real hoists and the industrial ease of use, EEMD is selected in this paper.…”

Section: Introductionmentioning

confidence: 99%

“…To improve the diagnosis accuracy, the selection of the main IMFs is necessary since not all IMFs are sensitive to the faults [ 23 ]. Some selection criteria have been proposed, such as the correlation coefficient criterion [ 22 ], the kurtosis criterion [ 24 ], and the similarity between the probability density function of the raw signal and that of each IMF [ 23 ]. However, they were all designed for rigid systems, especially bearings.…”

Section: Introductionmentioning

confidence: 99%

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Rope Tension Fault Diagnosis in Hoisting Systems Based on Vibration Signals Using EEMD, Improved Permutation Entropy, and PSO-SVM

Xue

Tan

Shi

et al. 2020

Entropy

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Fault diagnosis of rope tension is significantly important for hoisting safety, especially in mine hoists. Conventional diagnosis methods based on force sensors face some challenges regarding sensor installation, data transmission, safety, and reliability in harsh mine environments. In this paper, a novel fault diagnosis method for rope tension based on the vibration signals of head sheaves is proposed. First, the vibration signal is decomposed into some intrinsic mode functions (IMFs) by the ensemble empirical mode decomposition (EEMD) method. Second, a sensitivity index is proposed to extract the main IMFs, then the de-noised signal is obtained by the sum of the main IMFs. Third, the energy and the proposed improved permutation entropy (IPE) values of the main IMFs and the de-noised signal are calculated to create the feature vectors. The IPE is proposed to improve the PE by adding the amplitude information, and it proved to be more sensitive in simulations of impulse detecting and signal segmentation. Fourth, vibration samples in different tension states are used to train a particle swarm optimization–support vector machine (PSO-SVM) model. Lastly, the trained model is implemented to detect tension faults in practice. Two experimental results validated the effectiveness of the proposed method to detect tension faults, such as overload, underload, and imbalance, in both single-rope and multi-rope hoists. This study provides a new perspective for detecting tension faults in hoisting systems.

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“…Furthermore, the vibration may also impact the structure life-time, component reliability, vehicle comfort, etc. The topics regarding collecting and analyzing the vibration and noise signals at some reference points for faulty diagnosis [2][3][4], power system health monitoring [5,6], structure modal analysis [7][8][9][10][11], and noise and vibration control [12][13][14] have been popular for decades. From this literature, it is also shown that in some circumstances of faulty diagnosis and noise control, the key information contained in the vibration and noise signals are presented as some special combinations of sinusoidal signals.…”

Section: Introductionmentioning

confidence: 99%

Research on Control Technologies for a High-Precision Multi-Source Vibration Simulation System

Chen

He³

et al. 2018

Energies

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Vehicles commonly suffer from the narrow-band noises and vibrations, usually a superposition of multiple sinusoidal signals, due to the excitations of engines, electrical motors, gear boxes, and other rotating mechanical parts. These excitations are transmitted to a reference point of some structure with certain transmission paths. The vibration signal measured at the reference point can be used for power system monitoring, fault diagnosis, modal analysis, noise analysis, etc. For convenience, researchers in a laboratory usually use shakers to generate expected narrow-band vibration signals acting on the vehicle structure reference points to simulate the vibration signals. However, there is a prominent difficulty in ensuring the amplitude and phase accuracy of each sub-frequency component simultaneously. In order to improve the accuracy of generating the expected vibration signal, this paper presents a multi-source vibration simulation control technology based on the tracking filter method. The main idea is to use the tracking filter to estimate the amplitude and phase of the target sub-frequency component accurately. Further, on the target sub-frequency, the drive signal of shakers is then corrected based on the amplitude and phase errors to achieve a more accurate target vibration signal. The amplitude and phase of each sub-frequency component in the excitation signal can be controlled independently. Compared with other Fast Fourier Transform (FFT)-based frequency domain analysis algorithms and numerical methods by solving the equations, the tracking filter method has a higher frequency resolution and higher accuracy. It can be easily realized in real time applications due to its simplicity. Finally, verification experiments are completed. The experimental results show that the multi-source vibration simulation control technology presented in this paper can achieve high-precision amplitude and phase on each sub-frequency component of the target vibration signals, which contain up to eight sub-frequency components.

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Diagnosis of Localized Faults in Multistage Gearboxes: A Vibrational Approach by Means of Automatic EMD-Based Algorithm

Cited by 16 publications

References 35 publications

A Reliable Fault Diagnosis Method for a Gearbox System with Varying Rotational Speeds

A Reliable Fault Diagnosis Method for a Gearbox System with Varying Rotational Speeds

Rope Tension Fault Diagnosis in Hoisting Systems Based on Vibration Signals Using EEMD, Improved Permutation Entropy, and PSO-SVM

Research on Control Technologies for a High-Precision Multi-Source Vibration Simulation System

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