Effective Maintenance Actions Research Articles

Reliability Analysis of a Helicopter Utility Hydraulic System Using Reliability-centered Maintenance and Bayesian Network

Purpose: Reliability-centered maintenance (RCM) provides a standardized procedure for maintenance decision-making. The Bayesian network (BN) is a powerful tool for estimating the posterior probability based on the likelihood and prior probability distribution of failures. This study demonstrates reliability analysis and maintenance decision-making using RCM and BN.BRMethods: Maintenance decisions are made following the RCM framework for the failure of the utility hydraulic system. Bayesian updating and reliability analysis can be performed using the BN model. Real cases of field failure and the survival data of AH-64E were used for this study. The common cause failure (CCF) mode was incorporated into the BN to consider aircraft battle damages.BRResults: Failure diagnostic ability was demonstrated, and the life of the system was obtained. The most effective maintenance action was suggested based on reliability calculation. In addition, the results offered support that CCF should be considered for reliability calculation.BRConclusion: Reliability analysis conducted using RCM and BN can prove beneficial for system-level maintenance decision-making. The BN with CCF revealed unforeseen risks of aircraft battle damage.

Context-Sensitive Modeling and Analysis of Cyber-Physical Manufacturing Systems for Anomaly Detection and Diagnosis

Cyber-physical manufacturing systems (CPMS) can be defined by the integration of control, network communication, and computing with a physical manufacturing process. In this work, we present a hybrid model of CPMS combining sensor data, context information, and expert knowledge. We used the identification of global operational states and a multimodel framework to improve anomaly detection and diagnosis. The anomaly detection is based on context-sensitive adaptive threshold limits. Root cause diagnosis is based on classification models and expert knowledge. The proposed approach was implemented using the Internet of Things (IoT) to extract data from a computer numerical control machine. Results showed that using a context-sensitive modeling strategy allowed to combine physics-based and data-driven models for residual analysis to detect an anomaly in the part, machine, or process. The identification of root cause was improved by adding context information in classification models to identify worn or broken tools and wrong material. Note to Practitioners—Anomaly detection and diagnosis of manufacturing equipment is a complex problem. Some of the challenges are complex machine dynamics and nonstationary operating conditions. This paper describes a framework for modeling manufacturing equipment using a combination of sensor data, context information, and system knowledge. The proposed modeling framework is used to improve anomaly detection for diagnostics using a context-sensitive strategy. This work aims to support more effective maintenance actions by identifying problems in the machine, part, or process. The modeling and anomaly detection strategy was used to identify anomalies in computer numerical control machines and can be extended to other equipment on the plant floor.

Analysis of Maintenance Service Contracts for Dump Trucks Used in Mining Industry with Simulation Approach

A mining company needs high availability of dump trucks used to haul mining materials. As a result, an effective maintenance action is required to keep the dump trucks in a good condition and hence reducing failure and downtime of the dump trucks. To carry out maintenance in-house requires a high intensive maintenance facility and high skilled maintenance specialists. Often, outsourcing maintenance is an economic option for the company. An external agent takes a proactive action with offering some maintenance contract options to the owner. The decision problem for the owner is to decide the best option and for the agent is to determine the optimal price for each option offered. A non-cooperative game-theory is used to formulate the decision problems for the owner and the agent. We consider that failure pattern of each truck follows a non-homogeneous Poisson process (NHPP) and a queueing theory with multiple servers is used to estimate the downtime. As it involves high complexity to model downtime using a queueing theory, then in this paper we use a simulation method. Furthermore, we conduct experiment to seek for the best number of maintenance facilities (servers) which minimises maintenance and penalty costs incurred to the agent.

SIMAP: Intelligent System for Predictive Maintenance: Application to the health condition monitoring of a windturbine gearbox

SIMAP is the abbreviated name for the Intelligent System for Predictive Maintenance. It is a software application addressed to the diagnosis in real-time of industrial processes. It takes into account the information coming in real-time from different sensors and other information sources and tries to detect possible anomalies in the normal behaviour expected of the industrial components. The incipient detection of anomalies allows for an early diagnosis and the possibility to plan effective maintenance actions. Also, the continuous monitoring performed allows for an estimation in a qualitative form of the health condition of the components. SIMAP is a general tool oriented to the diagnosis and maintenance of industrial processes, however the first experience of its application has been at a windfarm. In this real case, SIMAP is able to optimize and to dynamically adapt a maintenance calendar for a monitored windturbine according to the real needs and operating life of it as well as other technical and economical criteria. In particular this paper presents the application of SIMAP to the health condition monitoring of a windturbine gearbox as an example of its capabilities and main features.

Optimal discrete and continuous maintenance policy for a complex unreliable machine

The failures of a complex unreliable machine depends on the age and the maintenance action history. Failures can be reduced (in a probabilistic sense) through effective maintenance actions. These maintenance actions can occur either at discrete time instants or continuously over time. Murthy (1984) dealt with a model that incorporates both these types of maintenance action and derived optimal maintenance policies using stochastic point process formulation and optimal control theory. This paper deals with a more general model. We give a complete characterization of the general model and derive some analytical results for two special cases.