Fault diagnosis of spur gearbox based on random forest and wavelet packet decomposition

Cabrera, Diego; Sancho, Fernando; Sánchez, René–Vinicio; Zurita, Grover; Cerrada, Mariela; Li, Chuan; Vásquez, Rafael E.

doi:10.1007/s11465-015-0348-8

Cited by 47 publications

(26 citation statements)

References 18 publications

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“…It depends only in one parameter C, that can be estimated in an analogous way as in Equation (12). Now that we have totally determined the distributions of both distances in terms of χ 2 distributions, p-values can be calculated, for a certain chosen critical level α, which are the probability of occurrence of each T 2 and Q.…”

Section: Outlier Detection Based On χ 2 Approximations Of Q and T 2 Smentioning

confidence: 99%

“…The magic behind RF is that the bias of the full ensemble is equivalent to the bias of each single tree, whereas the variance is much smaller. This robustness, together with its low computational cost and high parallelization and distribution capabilities makes RF an algorithm to be taken into consideration in predictive maintenance [43,127,12].…”

Section: Classificationmentioning

confidence: 99%

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On the Relevance of Preprocessing in Predictive Maintenance for Dynamic Systems

Cernuda

2019

Predictive Maintenance in Dynamic Systems

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The complexity involved in the process of real-time data-driven monitoring dynamic systems for predicted maintenance is usually huge. With more or less in-depth any data-driven approach is sensitive to data preprocessing, understood as any data treatment prior to the application of the monitoring model, being sometimes crucial for the final development of the employed monitoring technique. The aim of this work is to quantify the sensitiveness of data-driven predictive maintenance models in dynamic systems in an exhaustive way.We consider a couple of predictive maintenance scenarios, each of them defined by some public available data. For each scenario, we consider its properties and apply several techniques for each of the successive preprocessing steps, e.g. data cleaning, missing values treatment, outlier detection, feature selection, or imbalance compensation. The pretreatment configurations, i.e. sequential combinations of techniques from different preprocessing steps, are considered together with different monitoring approaches, in order to determine the relevance of data preprocessing for predictive maintenance in dynamical systems.

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Section: Outlier Detection Based On χ 2 Approximations Of Q and T 2 Smentioning

confidence: 99%

Section: Classificationmentioning

confidence: 99%

On the Relevance of Preprocessing in Predictive Maintenance for Dynamic Systems

Cernuda

2019

Predictive Maintenance in Dynamic Systems

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“…For all experiments, we measured out of sample performance using the Area under the ROC Curve (AUC) from 5-fold cross-validation. After training the random forest, we used the feature importances for feature selection [14,49]. The results show how the accuracy of the random forest changed as a factor of the number of features used, starting with the 13 maximum features and subsequently removing the less important features.…”

Section: Experimental Setup For Classificationmentioning

confidence: 99%

Fault detection of broken rotor bar in LS-PMSM using random forests

et al. 2018

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Abstract-This paper proposes a new approach to diagnose broken rotor bar failure in a line start-permanent magnet synchronous motor (LS-PMSM) using random forests. The transient current signal during the motor startup was acquired from a healthy motor and a faulty motor with a broken rotor bar fault. We extracted 13 statistical time domain features from the startup transient current signal, and used these features to train and test a random forest to determine whether the motor was operating under normal or faulty conditions. For feature selection, we used the feature importances from the random forest to reduce the number of features to two features. The results showed that the random forest classifies the motor condition as healthy or faulty with an accuracy of 98.8% using all features and with an accuracy of 98.4% by using only the mean-index and impulsion features. The performance of the random forest was compared with a decision tree, Naïve Bayes classifier, logistic regression, linear ridge, and a support vector machine, with the random forest consistently having a higher accuracy than the other algorithms. The proposed approach can be used in industry for online monitoring and fault diagnostic of LS-PMSM motors and the results can be helpful for the establishment of preventive maintenance plans in factories.

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“…However, no direct comparison to only using the FFT was performed. The WPT has been employed as a preprocessing tool into several different machine learning diagnostics tools, eg, such as neural networks and random forest . However, no singular apparent alternative of a machine learning tool to replace the expert operator has shown to be attractive enough to make it into the industry, as the computational effort and implementation at a large scale is still difficult .…”

Section: Introductionmentioning

confidence: 99%

Bearing monitoring in the wind turbine drivetrain: A comparative study of the FFT and wavelet transforms

et al. 2020

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Wind turbines are often plagued by premature component failures, with drivetrain bearings being particularly subjected to these failures. To identify failing components, vibration condition monitoring has emerged and grown substantially. The fast Fourier transform (FFT) is the major signal processing method of vibrations. Recently, the wavelet transforms have been used more frequently in bearing vibration research, with one alternative being the discrete wavelet transform (DWT). Here, the low-frequency component of the signal is repeatedly decomposed into approximative and detailed coefficients using a predefined mother wavelet. An extension to this is the wavelet packet transform (WPT), which decomposes the entire frequency domain and stores the wavelet coefficients in packets. How wavelet transforms and FFT compare regarding fault detection in wind turbine drivetrain bearings has been largely overlooked in literature when applied on field data, with non-ideal placement of sensors and uncertain parameters influencing the measurements. This study consists of a comprehensive comparison of the FFT, a three-level DWT, and the WPT when applied on enveloped vibration measurements from two 2.5-MW wind turbine gearbox bearing failures. The frequency content is compared by calculating a robust condition indicator by summation of the harmonics and shaft speed sidebands of the bearing fault frequencies. Results show a higher performance of the WPT when used as a field vibration measurement analysis tool compared with the FFT as it detects one bearing failure earlier and more clearly, leading to a more stable alarm setting and avoidable, costly false alarms. KEYWORDS bearing failure, condition monitoring, discrete wavelet transform, wavelet packet transform, wind turbine gearbox bearings 1 INTRODUCTION Wind power is today the fastest growing renewable energy source in the world, with an installed capacity of 591 GW in 2018 and a predicted growth up to 908 GW in 2023. 1 However, wind turbines designed for a 20-year lifetime still experience premature failures with the root cause not yet fully understood. When compiling failures occurring in all the subsystems within the wind turbine, gearbox failures have been shown to cause the longest downtime and are thereby also associated with the highest cost per failure. 2,3 Out of the two main component types, the bearings experience most failures, around 76% of the time and with the gearbox output and generator shaft bearings being most represented, while the gears fail 17% of the time and other sources 7%. 4The method considered most effective to minimize the costs of these failures in rotating equipment is condition monitoring, where vibrations is the most common method as it can give early warnings on the health of bearings and gears before their degradation threaten the surrounding components. 5 Statistical methods in the time domain as well as frequency domain methods such as the fast Fourier transform (FFT) has throughoutThe peer review history for this article is available at...

show abstract

Fault diagnosis of spur gearbox based on random forest and wavelet packet decomposition

Cited by 47 publications

References 18 publications

On the Relevance of Preprocessing in Predictive Maintenance for Dynamic Systems

On the Relevance of Preprocessing in Predictive Maintenance for Dynamic Systems

Fault detection of broken rotor bar in LS-PMSM using random forests

Bearing monitoring in the wind turbine drivetrain: A comparative study of the FFT and wavelet transforms

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