Diagnostics of the drive shaft bearing based on vibrations in the high-frequency range as a part of the vehicle's self-diagnostic system

Nowakowski, Tomasz; Komorski, Paweł

doi:10.17531/ein.2022.1.9

Cited by 7 publications

(4 citation statements)

References 33 publications

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“…Experimental studies and analysis of the results of the method showed high efficiency in detecting wheel flattening and ovalization errors. In [ 19 ], a new diagnostic algorithm was proposed to evaluate the condition of the bearings of the drive shaft of a road vehicle during operation. The diagnostic parameter was selected based on vibration measurements and the results of machine learning analysis.…”

Section: Related Work and Other Methodsmentioning

confidence: 99%

Diagnosis of the Pneumatic Wheel Condition Based on Vibration Analysis of the Sprung Mass in the Vehicle Self-Diagnostics System

Prażnowski

Mamala

Deptuła

et al. 2023

Sensors

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This paper presents a method for the multi-criteria classification of data in terms of identifying pneumatic wheel imbalance on the basis of vehicle body vibrations in normal operation conditions. The paper uses an expert system based on search graphs that apply source features of objects and distances from points in the space of classified objects (the metric used). Rules generated for data obtained from tests performed under stationary and road conditions using a chassis dynamometer were used to develop the expert system. The recorded linear acceleration signals of the vehicle body were analyzed in the frequency domain for which the power spectral density was determined. The power field values for selected harmonics of the spectrum consistent with the angular velocity of the wheel were adopted for further analysis. In the developed expert system, the Kamada–Kawai model was used to arrange the nodes of the decision tree graph. Based on the developed database containing learning and testing data for each vehicle speed and wheel balance condition, the probability of the wheel imbalance condition was determined. As a result of the analysis, it was determined that the highest probability of identifying wheel imbalance equal to almost 100% was obtained in the vehicle speed range of 50 km/h to 70 km/h. This is known as the pre-resonance range in relation to the eigenfrequency of the wheel vibrations. As the vehicle speed increases, the accuracy of the data classification for identifying wheel imbalance in relation to the learning data decreases to 50% for the speed of 90 km/h.

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Section: Related Work and Other Methodsmentioning

confidence: 99%

Diagnosis of the Pneumatic Wheel Condition Based on Vibration Analysis of the Sprung Mass in the Vehicle Self-Diagnostics System

Prażnowski

Mamala

Deptuła

et al. 2023

Sensors

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show abstract

“…Based on the information criterion, two examples of the best-fitting model are selected. Nowakowski T [13] et al proposed a new diagnostic algorithm for evaluating the performance of road vehicle drive shaft bearings in use, determining the upper and lower limits of diagnostic parameters, and predicting the likelihood of impending bearing wear and damage. An unsupervised monitoring method for rotary machines was proposed by Bielecki A [2] et al This method consists of three stages: a multi-reference preliminary analysis of vibration signals, an automatic reference preliminary analysis, and a probabilistic analysis of vibration signals.…”

Section: Related Workmentioning

confidence: 99%

“…In the following equation ( 13), the back propagation method is used to update the network parameter 𝜃 , 𝑥 𝑖 represents the input value for the corresponding layer in the network. 𝜕𝑥 𝑖 (13) The selection of the appropriate optimizer and learning rate warmup method. Optimizers and learning rates are used.…”

Section: Tsml-net For Condition Monitoringmentioning

confidence: 99%

Camera-based PHM method in rotating machinery equipment micro-action scenarios

An¹,

liu²,

Hao³

et al. 2023

Eksploatacja i Niezawodność – Maintenance and Reliability

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The health operation of rotating machinery guarantees safety of the project. To ensure a good operating environment, current subway equipment inspections frequency is high, resulting in a waste of resources. Small abnormal changes in mechanical equipment will also contribute to the development of mechanical component defects, which will ultimately lead to the failure of the equipment. Therefore, mechanical equipment defects should be detected and diagnosed as soon as possible. Through the use of graphic processing and deep learning, this paper proposes Rmcad Framework with three aspects: condition monitoring, anomaly detection, defect early warning. Using a network algorithm, this paper proposes an improved model that has the characteristics of two-stream and multi-loss functions, which improves the accuracy of detection. Additionally, a defect warning method is constructed to improve the perception ability of equipment before failure occurs and reduce the frequency of frequent maintenance by detecting anomalies according to the degree of opening.

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“…Empirical research on systems in which thermodynamic processes are carried out, characterized by high dynamics of changes in parameters and indicators of their operation, is conducted based on the assumptions of obtaining the most reliable measures and characteristics defining them [1,4,8]. Their purpose is not only the ongoing assessment of the quality of main processes, which is based on the analysis of the overall efficiency of the facility for specific operating conditions but also the process of generating and forecasting changes in operational characteristics with the time and intensity of use of the object in stationary and nonstationary conditions [22,27,28]. Thanks to this, it is possible to fill the multidimensional working space of the object with the representation of processes of a specific nature and properties observed there.…”

Section: Introductionmentioning

confidence: 99%

Assessment of the effectiveness of mounting the aircraft piston combustion engine on a test stand

Szymański,

Waligórski,

Misztal

2024

Combustion Engines

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The subject of the considerations described in the paper is the problem of efficiency of mounting an aircraft engine on a dyno test bench. The paper presents the empirical research results of vibroacoustic signal application to the evaluation of correctness of positioning of the Rotax 912 piston aircraft engine. The variability of selected parameters and vibroacoustic characteristics for real engine operating conditions was assessed, and functional relations and their discrete representations were developed, reflecting the efficiency of mounting the object on the frame. Thanks to this process, quantitative measures of the diagnostic assessment of the object were obtained, which can be used in periodic or continuous operational control of the object, taking into account the impact of tribological processes taking place in the test object.

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Diagnostics of the drive shaft bearing based on vibrations in the high-frequency range as a part of the vehicle's self-diagnostic system

Cited by 7 publications

References 33 publications

Diagnosis of the Pneumatic Wheel Condition Based on Vibration Analysis of the Sprung Mass in the Vehicle Self-Diagnostics System

Diagnosis of the Pneumatic Wheel Condition Based on Vibration Analysis of the Sprung Mass in the Vehicle Self-Diagnostics System

Camera-based PHM method in rotating machinery equipment micro-action scenarios

Assessment of the effectiveness of mounting the aircraft piston combustion engine on a test stand

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