Vibration-based diagnostics of epicyclic gearboxes – From classical to soft-computing methods

Jabłoński, Adam; Dworakowski, Ziemowit; Dziedziech, Kajetan; Chaari, Fakher

doi:10.1016/j.measurement.2019.07.039

Cited by 25 publications

(8 citation statements)

References 200 publications

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“…Benkedjouh et al [7] designed a rotating machinery fault prediction and health management method by vibration signals. Furthermore, vibration analysis does not invade the engine block and can detect multiple kinds of faults, so it is currently considered to be one of the strongest potential methods [8]. The engine has many excitation sources; hence, sensors are usually placed on the block and cylinder head cover to collect vibration signals synthetically.…”

Section: Introductionmentioning

confidence: 99%

An Engine Fault Detection Method Based on the Deep Echo State Network and Improved Multi-Verse Optimizer

Zhang

et al. 2022

Energies

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This paper aims to develop an efficient pattern recognition method for engine fault end-to-end detection based on the echo state network (ESN) and multi-verse optimizer (MVO). Bispectrum is employed to transform the one-dimensional time-dependent vibration signal into a two-dimensional matrix with more impact features. A sparse input weight-generating algorithm is designed for the ESN. Furthermore, a deep ESN model is built by fusing fixed convolution kernels and an autoencoder (AE). A novel traveling distance rate (TDR) and collapse mechanism are studied to optimize the local search of the MVO and speed it up. The improved MVO is employed to optimize the hyper-parameters of the deep ESN for the two-dimensional matrix recognition. The experiment result shows that the proposed method can obtain a recognition rate of 93.10% in complex engine faults. Compared with traditional deep belief networks (DBNs), convolutional neural networks (CNNs), the long short-term memory (LSTM) network, and the gated recurrent unit (GRU), this novel method displays superior performance and could benefit the fault end-to-end detection of rotating machinery.

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Section: Introductionmentioning

confidence: 99%

An Engine Fault Detection Method Based on the Deep Echo State Network and Improved Multi-Verse Optimizer

Zhang

et al. 2022

Energies

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“…The foundation of each system is usually the analysis of vibration signals recorded from the machine. Based on them and an in-depth analysis of the diagnostics problem for similar structures, the processing algorithms are determined to extract features that allow for a proper assessment of the structure state [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy-Logic-Based Recommendation System for Processing in Condition Monitoring

Górski

Heesch

Dziendzikowski

et al. 2022

Sensors

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The development of a machine’s condition monitoring system is often a challenging task. This process requires the collection of a sufficiently large dataset on signals from machine operation, context information related to the operation conditions, and the diagnosis experience. The two referred problems are today relatively easy to solve. The hardest to describe is the diagnosis experience because it is based on imprecise and non-numerical information. However, it is essential to process acquired data to develop a robust monitoring system. This article presents a framework for a system dedicated to recommending processing algorithms for condition monitoring. It includes a database and fuzzy-logic-based modules composed within the system. Based on the contextual knowledge provided by the user, the procedure suggests processing algorithms. This paper presents the evaluation of the proposed agent on two different parallel gearboxes. The results of the system are processing algorithms with assigned model types. The obtained results show that the algorithms recommended by the system achieve a higher accuracy than those selected arbitrarily. The results obtained allow for an average of 5 to 14.5% higher accuracy.

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“…It is therefore important to use condition monitoring methods to detect and characterise the damage as early as possible. Vibration-based condition monitoring methods are widely used for gearbox condition monitoring, because the measured vibration signals contain much information related to the instantaneous condition of the machine and it has relatively low costs involved with their implementation [3,4]. However, it can be difficult to observe the fault signatures associated with the damaged components, because they can be masked by time-varying operating con-ditions [4][5][6], impulsive noise [7] and dominant deterministic phenomena such as gear mesh interactions [8].…”

Section: Introductionmentioning

confidence: 99%

Combining an optimisation-based frequency band identification method with historical data for novelty detection under time-varying operating conditions

Schmidt

Gryllias

2021

Measurement

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Vibration-based diagnostics of epicyclic gearboxes – From classical to soft-computing methods

Cited by 25 publications

References 200 publications

An Engine Fault Detection Method Based on the Deep Echo State Network and Improved Multi-Verse Optimizer

An Engine Fault Detection Method Based on the Deep Echo State Network and Improved Multi-Verse Optimizer

Fuzzy-Logic-Based Recommendation System for Processing in Condition Monitoring

Combining an optimisation-based frequency band identification method with historical data for novelty detection under time-varying operating conditions

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