Engine valve clearance diagnostics based on vibration signals and machine learning methods

Tabaszewski, Maciej; Szymański, Grzegorz M.

doi:10.17531/ein.2020.2.16

Cited by 16 publications

(10 citation statements)

References 26 publications

(25 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The vibration process is a phenomenon whose changes during the operation of various objects are symptoms of failures and wear. It was analyzed in [9][10][11]. The work [9] covers the problems of recognizing the type and determining the degree of operational damage to the gear transmission.…”

Section: Literature Backgroundmentioning

confidence: 99%

See 1 more Smart Citation

The use of artificial intelligence methods in the analysis of the results of vehicle braking deceleration tests in the diagnostics of the braking system of a motor vehicle

Dobaj

2022

tren

View full text Add to dashboard Cite

The article presents the concept of using an artificial neural network to approximate the parameters describing the vehicle braking process, from the point of view of the application of this method in the diagnostics of the braking system. The artificial neural network of non-linear autoregression was used to approximate the dependence of the braking deceleration and the pressure in the braking system. The effectiveness of the neural network was checked depending on the number of neurons in its hidden layer and on the applied learning algorithm. The operation of the neural network was verified based on the actual braking processes of the Skoda Octavia, carried out with different dynamics, with different car weights and different tire inflation pressures. After verifying the neural network, it was used to approximate the braking deceleration values for the pressure values exceeding those present in the input data set. This action allows the analysis of the possibility of the vehicle obtaining a braking deceleration, which qualifies its braking system as efficient. Two concepts of using a neural network to solve this problem were analyzed. Conclusions related to the validity of the development of the discussed methods were drawn.

show abstract

Section: Literature Backgroundmentioning

confidence: 99%

“…The work [11] uses the analysis of vibration signals to evaluate the valve clearance in the internal combustion engine of a motor vehicle. A three-axis acceleration sensor was used, mounted on the engine head.…”

Section: Literature Backgroundmentioning

confidence: 99%

The use of artificial intelligence methods in the analysis of the results of vehicle braking deceleration tests in the diagnostics of the braking system of a motor vehicle

Dobaj

2022

tren

View full text Add to dashboard Cite

show abstract

“…The current fault diagnosis methods can be summarized into four categories [8,9]: knowledge-based fault diagnosis [10][11][12], model-based fault diagnosis [13][14][15], signalbased fault diagnosis [16][17][18], and hybrid method-based fault diagnosis (a method that combines two or more methods) [19][20][21][22]. Fault diagnosis for machining centres mainly include diagnosis methods based on fault information monitoring, training models, and fault trees.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Analysis Method for Fault Propagation Behaviour of Machining Centres

Zhang

Liu

et al. 2021

Applied Sciences

View full text Add to dashboard Cite

Fault propagation behaviour analysis is the basis of fault diagnosis and health maintenance. Traditional fault propagation studies are mostly based on a priori knowledge of a causality model combined with rule-based reasoning, disregarding the limitations of experience and the dynamic characteristics of the system that cause deviations in the identification of critical fault sources. Thus, this paper proposes a dynamic analysis method for fault propagation behaviour of machining centres that combines fault propagation mechanisms with model structure characteristics. This paper uses the design structure matrix (DSM) to establish the fault propagation hierarchy structure model. Considering the correlation of fault time, the fault probability function of a component is obtained and the fault influence degree of nodes are calculated. By introducing the Copula and Coupling degree functions, the fault influence degree of the edges between the same level and different levels are calculated, respectively. This paper constructs a fault propagation intensity model by integrating the edge betweenness and uses it as an index to analyze real-time fault propagation behaviour. Finally, a certain type of machining centre is taken as an example for specific application. This study can provide as a reference for the fault maintenance and reliability growth of a machining centre.

show abstract

“…Gan, Zhao and Chow also utilized vibration signals in electrical and mechanical motor fault detection under different frequency conditions using genetic algorithms and achieved a maximum of 93.96% test accuracy for electrical faults under 35 Hz frequency and a maximum of 96.9% test accuracy for mechanical faults under 45 Hz frequency [9]. One of the most recent studies using vibration signals for diagnostics was presented by Tabaszewski and Szymański, which proposes a set of binary tree-based classification for three valve clearance classes, listed as: tight, optimum and excess [39]. In [39], the classification accuracy achieved was 99%.…”

Section: Introductionmentioning

confidence: 99%

Induction motor fault classification via entropy and column correlation features of 2D represented vibration data

Başaran¹,

Fidan²

2021

Eksploatacja i Niezawodność – Maintenance and Reliability

View full text Add to dashboard Cite

Due to long-term use under challenging conditions, the sub-elements of induction motors may suffer certain defects over time. Such defects impair the vibration characteristics of the motors in different ways, depending on the type of defect. Therefore, the change in vibration characteristic provides indicators about the fault type and can be used in preventive maintenance strategies to ensure safe operation of the system. In this work, discrete-time vibration data were transformed into 2-dimensional grey-level images and decomposed into individual components by the Wavelet decomposition method. Features based on entropy and column correlation were extracted from these components and used to classify motor faults by using the Support Vector Machine method implemented by using the Sequential Minimal Optimisation algorithm. When the selected classifier is compared with other popular Machine Learning algorithms, it is observed that motor faults are more successfully classified, and these observations are presented in detail with comparative classification performance results.

show abstract

Engine valve clearance diagnostics based on vibration signals and machine learning methods

Abstract: Article citation info: (*) Tekst artykułu w polskiej wersji językowej dostępny w elektronicznym wydaniu kwartalnika na stronie www.ein.org.pl

Cited by 16 publications

References 26 publications

The use of artificial intelligence methods in the analysis of the results of vehicle braking deceleration tests in the diagnostics of the braking system of a motor vehicle

The use of artificial intelligence methods in the analysis of the results of vehicle braking deceleration tests in the diagnostics of the braking system of a motor vehicle

Dynamic Analysis Method for Fault Propagation Behaviour of Machining Centres

Induction motor fault classification via entropy and column correlation features of 2D represented vibration data

Contact Info

Product

Resources

About