An optimization-based identification study of cylindrical floating ring journal bearing system in automotive turbochargers

Mutra, Rajasekhara Reddy; Srinivas, J.

doi:10.1007/s11012-022-01507-7

Cited by 7 publications

(2 citation statements)

References 44 publications

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“…These features have established ANN as one of the best tools available for defect diagnosis. Recently, Mutra et al [30,31] explained the use of artificial neural network-based parameter identification and its optimisation in high-speed rotor bearing systems. Mba et al [32] combined stochastic resonance to amplify weak impulses using noise and hidden Markov modelling (HMM) to model the system observation as a probabilistic function.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network-Based Fault Diagnosis of Gearbox using Empirical Mode Decomposition from Vibration Response

Reddy Mutra,

D Mallikarjuna Reddy,

Amarnath

et al. 2023

Int. J. Automot. Mech. Eng.

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This paper presents a gearbox defect diagnosis based on vibration behaviour. In order to record the vibration response under various circumstances, an industrial gearbox was used as the basis for an experimental setup. The signals resulting from gear wear were processed using an empirical mode decomposition for two operating time intervals (zero-hour running time and thirty-hour running time). The first three intrinsic mode functions and the corresponding frequency response were detected. The ten statistical parameters most sensitive to gear wear were selected using an evaluation method based on Euclidean distance. Using the identified features, an artificial neural network (ANN) was trained to track the gearbox for the selected future data set. The neural network received its input from the statistical parameters, and its output was the number of gearbox running hours. To achieve faster convergence, the radial basis function and the backpropagation neural network were compared. The superiority of the proposed strategy is demonstrated by comparing the performance of ANN. For monitoring the condition of industrial gears, the proposed strategy is found to be effective and trustworthy.

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Section: Introductionmentioning

confidence: 99%

Artificial Neural Network-Based Fault Diagnosis of Gearbox using Empirical Mode Decomposition from Vibration Response

Reddy Mutra,

D Mallikarjuna Reddy,

Amarnath

et al. 2023

Int. J. Automot. Mech. Eng.

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show abstract

“…To predict the optimal bearing parameters, a surrogate model was used combined with a trained backpropagation neural network model, which turned out to be more efficient than other models. The study [29] developed a methodology to predict the bearing coefficients, and the study [27] investigated and modelled the stability of the TCR rotor system using a neural network model and proposed the optimal design of the bearing foil parameters.…”

Section: Introductionmentioning

confidence: 99%

Studying the Temperature Characteristics of Oil at the Outlet From the K27-145 Turbocharger Rotor Bearing

Гриценко¹,

Shepelev²,

Kaliyev³

2022

Tribol. Ind.

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Turbocharging is a promising solution for increasing the specific power of motor-and-tractor equipment, which allows increasing power up to 50%. At the same time, a significant growth of speed, load, and temperature modes leads to a significant increase in the number of failures and a decrease in reliability by 2-3 times. Theoretical and experimental studies established that the temperature of the bearing and oil at the turbocharger drain changes under the influence of the inlet oil temperature to the TCR bearing, the TCR rotor shaft speed, and the change in the oil pressure at the input to the TCR bearing. This allows setting the limits of the TCR bearing performance in extreme operating conditions. The installation of an autonomous TCR lubricating and braking device maintains the required reliability level and increases failure-free operation. The hydraulic accumulator installed in the lubrication system of the engine turbocharger regulars lubricates and cools the rotor bearings when the ICE crankshaft speed drops. The incorporation of a braking device reduces the rundown time of the rotor and thereby prevents oil starvation and dry friction of the rotor bearing. The combined use of the hydraulic accumulator and the braking device minimizes the risk of dry friction and accidental failure of the turbocharger. We proved that the braking device of the TCR rotor built into the intake system of an ICE with the calculated design parameters reduces the rotor rundown time by 30-35%. This reduces the dimensions and operating time of the hydraulic accumulator and simultaneously eliminates surges in the compressor section of the TCR and any breakage of its parts. In these conditions, it is relevant to develop independent systems to lubricate the TCR bearings and replenish them using built-in hydraulic accumulators during start-up, significant loads at minimum crankshaft speeds, and engine stalling.

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Signal-based parameter and fault identification in roller bearings using adaptive neuro-fuzzy inference systems

Mutra

Reddy

Srinivas

et al. 2022

J Braz. Soc. Mech. Sci. Eng.

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An optimization-based identification study of cylindrical floating ring journal bearing system in automotive turbochargers

Cited by 7 publications

References 44 publications

Artificial Neural Network-Based Fault Diagnosis of Gearbox using Empirical Mode Decomposition from Vibration Response

Artificial Neural Network-Based Fault Diagnosis of Gearbox using Empirical Mode Decomposition from Vibration Response

Studying the Temperature Characteristics of Oil at the Outlet From the K27-145 Turbocharger Rotor Bearing

Signal-based parameter and fault identification in roller bearings using adaptive neuro-fuzzy inference systems

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