Detection of gear pitting failure progression with on-line particle monitoring

Kattelus, J.; Miettinen, Juha; Lehtovaara, Arto

doi:10.1016/j.triboint.2017.02.045

Cited by 58 publications

(36 citation statements)

References 8 publications

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“…where p f is the fatigue limit in terms of pressure and a s = a 0.6 . As a consequence, Equation (23) can also be rearranged as well as a function of the contact pressure, as shown in Equation (25).…”

Section: Methods For Surface Fatigue Damage Evaluationmentioning

confidence: 99%

“…According to the American Society for Metals (ASM) handbook, "Usually, pitting is the result of surface cracks caused by metal-to-metal contact of asperities or defects due to low lubricant film thickness" [24]. Even if the starting point of pitting phenomena is still under investigation by the scientific community, the link between bodies working in contact and pitting issues is real and approved without doubts [25].…”

Section: Introductionmentioning

confidence: 99%

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On the Evaluation of Surface Fatigue Strength of a Stainless-Steel Aeronautical Component

Cianetti

Ciotti

Palmieri

et al. 2019

Metals

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In this paper, a novel method for the evaluation of the surface fatigue strength of a stainless-steel component is proposed. The use of stainless steel is necessary indeed, whenever a component has to work in a particularly aggressive environment that may cause an oxidation of the component itself. One of the major problems that affect stainless-steel components is the possible wear of the antioxidant film that reduces the antioxidant properties of the component itself. One of the main causes that can lead to wear is related to the surface corrosion that occurs every time two evolving bodies are forced to work against each other. If the antioxidant film is affected by surface fatigue problems, such as pitting or spalling, the antioxidant capacities of this type of steel may be lost. In this context, it is, therefore, necessary to verify, at least, by calculation that no corrosion problems exist. The method proposed in this activity is a hybrid method, numerical-theoretical, which allows to estimate the surface fatigue strength in a very short time without having to resort to finite element models that often are so complex to be in contrast with industrial purposes.

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Section: Methods For Surface Fatigue Damage Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On the Evaluation of Surface Fatigue Strength of a Stainless-Steel Aeronautical Component

Cianetti

Ciotti

Palmieri

et al. 2019

Metals

View full text Add to dashboard Cite

show abstract

“…Irrespective of the severity of a gear wheel (tooth) failure, the repair of the gear wheel is often impossible; in that case, the whole damaged gear wheel or, very often, several mating gear wheels need to be replaced. The most frequent causes of gear wheel failure include tooth stresses, surface pressures and occurring friction [7,15,16]. Due to the effect of the above-mentioned factors, tooth failures may occur, which include primarily abrasive wear, spalling, gear seizure and pitting.…”

Section: Tooth Failure Typesmentioning

confidence: 99%

Diagnosis of the Operational Gear Wheel Wear

2019

Teh. vjesn.

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The article presents an analysis in the field of gear wheel testing method application. The results of tests of gear wheel teeth taken from a damaged gear rim are reposted. Tests were carried out on specimens to establish the causes of gear wheel tooth cracking. Macroscopic examination was made, which confirmed the occurrence of top layer spalling. An analysis to examine the damage to the surfaces of mating gear wheels is described. A destructive testing method was employed. The results of hardness and microhardness tests and metallographic analysis are presented.

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“…There is no general indication of the minimum training collection times required to accurately describe a set of condition indicators, which largely depend on the characteristics of the distribution. Kattelus et al [2] found that the vibration acceleration descriptor of the peak signal was related to the pitting of the gear contact. Traditional vibrational signals are significantly affected by the external environment.…”

Section: Introductionmentioning

confidence: 99%

Gear Pitting Fault Diagnosis Using Integrated CNN and GRU Network with Both Vibration and Acoustic Emission Signals

et al. 2019

Applied Sciences

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This paper deals with gear pitting fault diagnosis problem and presents a method by integrating convolutional neural network (CNN) and gated recurrent unit (GRU) networks with vibration and acoustic emission signals to solve the problem. The presented method first trains a one-dimensional CNN with acoustic emission signals and a GRU network with vibration signals. Then the gear pitting fault features obtained by the two networks are concatenated to form a deep learning structure for gear pitting fault diagnosis. Seven different gear pitting conditions are used to test the feasibility of the presented method. The diagnosis result of the gear pitting fault shows that the accuracy of the presented method reaches above 98% with only a relatively small number of training samples. In comparison with the results using CNN or GRU network alone, the presented method gives more accurate diagnosis results. By comparing the results of different loads and learning rates, the robustness of the presented method for gear pitting fault diagnosis is proved. Moreover, the presented deep structure can be easily extended to more other sensor input signals for gear pitting fault diagnosis in the future.

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Detection of gear pitting failure progression with on-line particle monitoring

Cited by 58 publications

References 8 publications

On the Evaluation of Surface Fatigue Strength of a Stainless-Steel Aeronautical Component

On the Evaluation of Surface Fatigue Strength of a Stainless-Steel Aeronautical Component

Diagnosis of the Operational Gear Wheel Wear

Gear Pitting Fault Diagnosis Using Integrated CNN and GRU Network with Both Vibration and Acoustic Emission Signals

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