Condition monitoring and state classification of gearboxes using stochastic resonance and hidden Markov models

Clement, Ute; Makiš, Viliam; Marchesiello, Stefano; Fasana, Alessandro; Garibaldi, Luigi

doi:10.1016/j.measurement.2018.05.038

Cited by 54 publications

(24 citation statements)

References 19 publications

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“…Li et al (2018) have developed a datadriven bearing fault identification methodology using an improved hidden Markov model and self-organizing map. Mba et al (2018) developed a hidden Markov model-based methodology for condition monitoring of gearbox. Zhang et al (2018) developed a methodology for predicting the residual life of the rolling machine elements using hidden Markov model.…”

Section: Case Studymentioning

confidence: 99%

Hidden Markov model-based digital twin construction for futuristic manufacturing systems

Ghosh

Ullah

Kubo

2019

AIEDAM

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This paper addresses the construction of digital twins (exact mirror images of real-world in cyberspace) using hidden Markov models for the futuristic manufacturing systems known as Industry 4.0. The proposed digital twin consists of two components namely model component and simulation component. The model component forms a Markov chain that encapsulates the dynamics underlying the phenomenon by using some discrete states and their transition probabilities. The simulation component recreates the phenomenon using a Monte Carlo simulation process. The efficacy of the proposed digital twin construction methodology is shown by a case study, where the digital twin of the surface roughness of a surface created by successive grinding operations is described. The developers of the cyber-physical systems will be benefitted from the outcomes of this study because these systems need the computable virtual abstractions of the manufacturing phenomena to address the issues related to the maturity index of futuristic manufacturing systems (i.e., understand, predict, decide, and adopt).

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Section: Case Studymentioning

confidence: 99%

Hidden Markov model-based digital twin construction for futuristic manufacturing systems

Ghosh

Ullah

Kubo

2019

AIEDAM

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“…Given the failures of this component, the gearboxes present in the cooling tower are already equipped with a monitoring system that can detect the moment the component changes from stable to degraded mode of operation, as shown in Figure 3, and sends a message, called an alarm, to the maintenance team. These condition-based monitoring systems are commercially available and largely described in literature [40][41][42][43], and thus will not be described here. By having this system already installed, however, it was possible to gather information about the time between a cell going into operation and the alarm message being sent, as well as the time between sending the alarm and the gearbox failure and the repair time.…”

Section: Gspn Model For the Cooling Towermentioning

confidence: 99%

Maintenance Strategy Optimization of a Coal-Fired Power Plant Cooling Tower through Generalized Stochastic Petri Nets

et al. 2019

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Determining the ideal size of maintenance staff is a daunting task, especially in the operation of large and complex mechanical systems such as thermal power plants. On the one hand, a significant investment in maintenance is necessary to maintain the availability of the system. On the other hand, it can significantly affect the profit of the plant. Several mathematical modeling techniques have been used in many different ways to predict and improve the availability and reliability of such systems. This work uses a modeling tool called generalized stochastic Petri net (GSPN) in a new way, aiming to determine the effect that the number of maintenance teams has on the availability and performance of a coal-fired power plant cooling tower. The results obtained through the model are confronted with a thermodynamic analysis of the cooling tower that shows the influence of this system’s performance on the efficiency of the power plant. Thus, it is possible to determine the optimal size of the repair team in order to maximize the plant’s performance with the least possible investment in maintenance personnel.

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“…SR was first proposed by Benzi to describe the periodicity associated with the Earth's ice ages in climatology in the 1980s [7]. is interesting nonlinear phenomenon makes it possible for the weak signals to strengthen their intensities by absorbing a certain fraction of the noise energy, thus highlighting the weak signals [8,9]. SR has attracted much attention from the physics and engineering community in the research areas such as dynamical nonlinearity [10][11][12], structure monitoring [13], and energy harvesting [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Rolling Bearing Fault Diagnosis Based on Adaptive Multiparameter-Adjusting Bistable Stochastic Resonance

Lai

Wang

Zhang

et al. 2020

Shock and Vibration

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The weak-signal detection technologies based on stochastic resonance (SR) play important roles in the vibration-based health monitoring and fault diagnosis of rolling bearings, especially at their early-fault stage. Aiming at the parameter-fixed vibration signals in practical engineering, it is feasible to diagnose the potential rolling bearing faults through adaptively adjusting the SR system parameters, as well as other generalized parameters such as the amplitude-transformation coefficient and scale-transformation coefficient. However, extant adaptive adjustment methods focus on the system parameters, while the adjustments of other adjustable parameters have not been fully studied, thus limiting the detection performance of the adaptive SR method. In order to further enhance the detection performance of adaptive SR methods and extend their application in rolling bearing fault diagnosis, an adaptive multiparameter-adjusting SR (AMPASR) method for bistable systems based on particle swarm optimization (PSO) algorithm is proposed in this paper. This method can produce optimal SR output through adaptively adjusting multiparameters, thus realizing fault feature extraction and further fault diagnosis. Furthermore, the influence of algorithm parameters on the optimization results is discussed, and the optimization results of the Langevin system and the Duffing system are compared. Finally, we propose a weak-signal detection method based on the AMPASR of the Duffing system and employ three diagnosis examples involving inner ring fault, outer ring fault, and rolling element fault diagnoses to demonstrate its feasibility in rolling bearing fault diagnosis.

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Condition monitoring and state classification of gearboxes using stochastic resonance and hidden Markov models

Cited by 54 publications

References 19 publications

Hidden Markov model-based digital twin construction for futuristic manufacturing systems

Hidden Markov model-based digital twin construction for futuristic manufacturing systems

Maintenance Strategy Optimization of a Coal-Fired Power Plant Cooling Tower through Generalized Stochastic Petri Nets

Rolling Bearing Fault Diagnosis Based on Adaptive Multiparameter-Adjusting Bistable Stochastic Resonance

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