Condition based maintenance-systems integration and intelligence using Bayesian classification and sensor fusion

Mehta, Parikshit; Werner, Andrew; Mears, Laine

doi:10.1007/s10845-013-0787-1

Cited by 42 publications

(9 citation statements)

References 20 publications

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“…In the context of maintenance strategies, the condition based approach is considered as effective [11], but also complex to implement and integrate to a production system [12]. However, the determination of the current equipment condition is an important issue in the maintenance process chain.…”

Section: Related Workmentioning

confidence: 99%

A Cloud-to-Edge Approach to Support Predictive Analytics in Robotics Industry

Panicucci¹,

Nikolakis

Cerquitelli

et al. 2020

Electronics

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Data management and processing to enable predictive analytics in cyber physical systems holds the promise of creating insight over underlying processes, discovering anomalous behaviours and predicting imminent failures threatening a normal and smooth production process. In this context, proactive strategies can be adopted, as enabled by predictive analytics. Predictive analytics in turn can make a shift in traditional maintenance approaches to more effective optimising their cost and transforming maintenance from a necessary evil to a strategic business factor. Empowered by the aforementioned points, this paper discusses a novel methodology for remaining useful life (RUL) estimation enabling predictive maintenance of industrial equipment using partial knowledge over its degradation function and the parameters that are affecting it. Moreover, the design and prototype implementation of a plug-n-play end-to-end cloud architecture, supporting predictive maintenance of industrial equipment is presented integrating the aforementioned concept as a service. This is achieved by integrating edge gateways, data stores at both the edge and the cloud, and various applications, such as predictive analytics, visualization and scheduling, integrated as services in the cloud system. The proposed approach has been implemented into a prototype and tested in an industrial use case related to the maintenance of a robotic arm. Obtained results show the effectiveness and the efficiency of the proposed methodology in supporting predictive analytics in the era of Industry 4.0.

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Section: Related Workmentioning

confidence: 99%

A Cloud-to-Edge Approach to Support Predictive Analytics in Robotics Industry

Panicucci¹,

Nikolakis

Cerquitelli

et al. 2020

Electronics

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“…In [20] Bayesian updating method was adopted to monitor degradation of bearings conditions in order to detect incipient fault and predict residual life of the bearing. Bayesian probabilistic model and naïve Bayes classifier have been implemented by [21], [22] for bearing condition monitoring and fault detection. Cascade correlation neural network classifier was found effective for vehicles door system degradation and failure detection [23].…”

Section: Related Work On Intelligent Maintenance Systemmentioning

confidence: 99%

“…The introduction of ANN that mimics the ability of a biological neuron in the human brains to learn and adapt the changing environment provides an intelligent solution, especially when there is no availability of exact physic-based mathematical models of the GTE system [28], [29]. Several methods have been used over the years to solve classification problems; these include statistical classification techniques, Bayesian classification approaches [21], [22], linear classifiers, nearest neighbourhood classifier, support vector machine and neural network based classification techniques.…”

Section: Gte Performance Classification For Incipient Fault Detectionmentioning

confidence: 99%

Reduction of impacts of oil and gas operations through intelligent maintenance solution

Eyoh

Kalawsky

2018

Proceedings of the International Conference on Intelligent Science and Technology

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Impacts of oil and gas production operations are always very obvious when there is imbalanced operation, uncontrolled stoppage or catastrophic failure of the system during normal operations. These impacts may range from high flaring and venting of associated petroleum gas, oil release or spillage, equipment damage, fire outbreak to even fatality. Possible causes of imbalanced operations or system failure are categorised into process upset, system degradation, ineffective operation and maintenance procedures and human errors. Effective maintenance strategy integrates major components of the system; people (human factors), operation and maintenance procedures (process) and production plant (technology) to develop an intelligent maintenance solution that is capable of monitoring and detecting fault in the system at incipient stage before operational integrity is compromised. This paper deploys data-based analytics technique to develop condition-based predictive maintenance system to monitor, predict and classify performance of gas processing system. Exhaust gas temperature (EGT) of Gas Turbine Engine (GTE) is one of the operating and control parameters associated with efficiency of the GTE operation. The EGT is measured using several thermocouples, temperature sensors spaced equidistant around the circumference of the exhaust duct of the GTE. Neural network technique of multisensory data fusion is integrated with intelligent maintenance system to monitor performance of GTE, detect fault and classify performance of GTE to optimal, average and abnormal performance.

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“…Data-based maintenance decision-making method is difficult to build for the same reason. The traditional reliability-based method, such as D-S evidence theory [ 33 , 34 , 35 , 36 ], Bayes theory [ 37 , 38 ], and fuzzy set theory [ 39 , 40 , 41 , 42 , 43 ], will face severe challenges with the uncertainty of information and variety of data types. When provided with conflicting evidence, the D-S evidence theory results tend to deviate from the understanding of the user.…”

Section: Introductionmentioning

confidence: 99%

Health Management Decision of Sensor System Based on Health Reliability Degree and Grey Group Decision-Making

Song

Wei

et al. 2018

Sensors

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Metal Oxide Semiconductor (MOS) gas sensor has been widely used in sensor systems for the advantages of fast response, high sensitivity, low cost, and so on. But, limited to the properties of materials, the phenomenon, such as aging, poisoning, and damage of the gas sensitive material will affect the measurement quality of MOS gas sensor array. To ensure the stability of the system, a health management decision strategy for the prognostics and health management (PHM) of a sensor system that is based on health reliability degree (HRD) and grey group decision-making (GGD) is proposed in this paper. The health management decision-making model is presented to choose the best health management strategy. Specially, GGD is utilized to provide health management suggestions for the sensor system. To evaluate the status of the sensor system, a joint HRD-GGD framework is declared as the health management decision-making. In this method, HRD of sensor system is obtained by fusing the output data of each sensor. The optimal decision-making recommendations for health management of the system is proposed by combining historical health reliability degree, maintenance probability, and overhaul rate. Experimental results on four different kinds of health levels demonstrate that the HRD-GGD method outperforms other methods in decision-making accuracy of sensor system. Particularly, the proposed HRD-GGD decision-making method achieves the best decision accuracy of 98.25%.

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Condition based maintenance-systems integration and intelligence using Bayesian classification and sensor fusion

Cited by 42 publications

References 20 publications

A Cloud-to-Edge Approach to Support Predictive Analytics in Robotics Industry

A Cloud-to-Edge Approach to Support Predictive Analytics in Robotics Industry

Reduction of impacts of oil and gas operations through intelligent maintenance solution

Health Management Decision of Sensor System Based on Health Reliability Degree and Grey Group Decision-Making

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