Dimensionality reduction via variables selection – Linear and nonlinear approaches with application to vibration-based condition monitoring of planetary gearbox

Diverse machines in the mining, energy, and other industrial sectors are subject to variable operating conditions (OCs) such as rotational speed and load. Therefore, the condition monitoring techniques must be adapted to face this scenario. Within these techniques, the acoustic emission (AE) technology has been successfully used as a technique for condition monitoring of components such as gears and bearings. An AE analysis involves the detection of transients within the signals, which are called AE bursts. Traditional methods for AE burst detection are based on the definition of threshold values. When the machine under study works under variable rotational speed and load, threshold-based methods could produce inadequate results due to the influence of these OCs on the AE. This paper presents a novel burst detection method based on pattern recognition using an artificial neural network (ANN) for classification. The results of the method were compared to an adaptive threshold method. Experimental data were measured in a planetary gearbox test rig under different OCs. The results showed that both methods perform similarly when signals measured under constant OCs are considered. However, when signals are measured under different OCs, the ANN method performs better. Thus, the comparative analysis showed the good potential of the approach to improve an AE analysis of variable speed and/or load machines.

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Learning and Recognition of Object Manipulation Actions Using Linear and Nonlinear Dimensionality Reduction

RO-MAN 2007 - The 16th IEEE International Symposium on Robot and Human Interactive Communication ◽

10.1109/roman.2007.4415230 ◽

2007 ◽

Cited By ~ 1

Author(s):

Isabel Serrano Vicente ◽

Danica Kragic ◽

Jan-Olof Eklundh

Keyword(s):

Dimensionality Reduction ◽

Object Manipulation ◽

Nonlinear Dimensionality Reduction ◽

Linear And Nonlinear

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Wind turbine planetary gearbox feature extraction and fault diagnosis using a deep-learning-based approach

Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability ◽

10.1177/1748006x18768701 ◽

2018 ◽

Vol 233 (3) ◽

pp. 303-316 ◽

Cited By ~ 4

Author(s):

Miao He ◽

David He ◽

Jae Yoon ◽

Thomas J Nostrand ◽

Junda Zhu ◽

...

Keyword(s):

Deep Learning ◽

Fault Diagnosis ◽

Condition Monitoring ◽

Dictionary Learning ◽

Memory Storage ◽

Vibration Monitoring ◽

Planetary Gearbox ◽

Storage And Retrieval ◽

Vibration Data ◽

Large Memory

Planetary gearboxes are widely used in the drivetrain of wind turbines. Planetary gearbox fault diagnosis is very important for reducing the downtime and maintenance cost and improving the safety, reliability, and life span of the wind turbines. The wind energy industry is currently using condition monitoring systems to collect massive real-time data and conventional vibratory analysis as a standard method for planetary gearbox condition monitoring. As an attractive option to process big data for fault diagnosis, deep learning can automatically learn features that otherwise require much skill, time, and experience. This article presents a new deep-learning-based method for wind turbine planetary gearbox fault diagnosis developed by a large memory storage and retrieval neural network with dictionary learning. The developed approach can automatically extract self-learned fault features from raw vibration monitoring data and perform planetary gearbox fault diagnosis without supervised fine-tuning process. From the raw vibration monitoring data, a dictionary is first learned by a large memory storage and retrieval with dictionary learning network. Based on the learned dictionary, a sparse representation of the raw vibration signals is generated by shift-invariant sparse coding and input to a large memory storage and retrieval network classifier to obtain fault diagnosis results. The structure of the large memory storage and retrieval with dictionary learning is determined by optimal selection of the sliding box size to generate sub-patterns from the vibration data. The effectiveness of the presented method is tested and validated with a set of seeded fault vibration data collected at a planetary gearbox test rig in laboratory. The validation results have shown a promising planetary gearbox fault diagnosis performance with the presented method.

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A Study on Defect Identification of Planetary Gearbox under Large Speed Oscillation

Mathematical Problems in Engineering ◽

10.1155/2016/5289698 ◽

2016 ◽

Vol 2016 ◽

pp. 1-18 ◽

Cited By ~ 8

Author(s):

Xingxing Jiang ◽

Shunming Li ◽

Qian Wang

Keyword(s):

Condition Monitoring ◽

Vibration Signal ◽

Estimation Methods ◽

Planetary Gearbox ◽

Time Frequency ◽

Ridge Estimation ◽

Practical Engineering ◽

Conventional Methods ◽

Tracking Time ◽

Large Speed

Rotational speed of a reference shaft is the key information for planetary gearbox condition monitoring under nonstationary conditions. As the time-variant speed and load of planetary gearboxes result in time-variant characteristic frequencies as well as vibration magnitudes, the conventional methods tracking time-frequency ridge perform a poor robustness, especially for large speed variations. In this paper, two schemes, time-frequency ridge fusion and logarithm transformation, are proposed to track the targeted ridge curve reliably. Meanwhile, the identified ridge curve by logarithm scheme can be further refined by the time-frequency ridge fusion scheme. Hence, a procedure involving the proposed ridge estimation methods is presented to diagnose the planetary gearbox defects. Two simulation signals and a vibration signal collected from a planetary gearbox in practical engineering (provided by the conference on condition monitoring of machinery in nonstationary operations (CMMNO)) are used to verify the proposed methods. It is validated that the proposed methods can well-track the targeted ridge curve compared with two conventional methods. As a result, the characteristic frequency of each component in the planetary gearbox is clearly demonstrated and the inner race defect of one of the planet bearings is successfully discovered in the order spectrum depending on the derived expression of planet bearing fault frequency.

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