Optimum Inspection Interval Research Articles

A BAYESIAN DATA UPDATING MODEL FOR MANAGING BUILDING ENVELOPE INSPECTIONS

Building envelope management includes all activities, e.g., periodic inspections, condition assessment and maintenance, needed to preserve the integrity of the building exterior. In building envelope management, appropriate decision-making focuses on finding an optimum inspection interval and repair strategy. A Bayesian updating with rating method is presented for conducting planning and decision-making for building envelop inspections using data from previous inspections. The model applies to a zone (strip) of the envelope and uses the previous inspection records to predict the future condition. An artificial neural network (ANN) is used to improve the prediction for the envelope condition. The ANN uses historical data of an envelope condition to determine a correction factor, for the predicted condition, to account for the effect of factors that promote damage. The Bayesian updating will improve the capabilities of the model in predicting the future conditions as more data becomes available. Used along with a cost optimization and reserve analysis, the model will result in a more sustainable and cost-effective building envelope management process.

Inspection interval optimization of aircraft landing gear component based on risk assessment using equivalent initial flaw size distribution method

The nondestructive inspection interval is highly related with both system reliability and maintenance burden. Conventional inspection interval decision criteria based on the deterministic crack propagation analysis could require too much frequent inspection or sometimes occur structural failure owing to the rapid crack propagation than expected. The stochastic crack growth analysis method was proposed to compensate for the shortcomings of the deterministic analysis. This research studied the crack growth of aircraft landing gear components based on the equivalent initial flaw size distribution algorithm, and then we assessed failure risk. The calculated risk was validated using Monte-Carlo simulation, and finally, the optimum inspection interval was proposed to satisfy the US Airforce risk management criteria.

An integration of multi-criteria decision making techniques with a delay time model for determination of inspection intervals for marine machinery systems

For certain critical equipment items in marine machinery systems, the optimum maintenance strategy would be a scheduled on-condition operation. This involves inspection of the equipment items in order to monitor their performance degradation and, invariably, carry out repair or replacement tasks. The main challenge with this type of maintenance approach is the determination of the appropriate interval for performing the inspection task. This paper presents a methodology which integrates multi-criteria decision making (MCDM) tools with a delay time model for the determination of optimum inspection intervals for marine machinery systems. With this approach, multiple decision criteria are modelled with the delay time concept and aggregated with MCDM tools such that different criteria can be applied simultaneously in the ranking of different inspection interval alternatives. The applicability of the proposed methodology is demonstrated using the case study of a water cooling pump of the central cooling system of a marine diesel engine.

Development of risk assessment model for equipment within the petroleum industry

Abstract: Maintenance department within petroleum industry seek to increase equipment safety by means of reducing the occurrence of the failure and its undesirable consequences. In this study, a risk assessment model is proposed, which includes the likelihood of the risk and the consequences of failure. A new mathematical equation is proposed to assess the likelihood of risk and identify the optimum inspection interval. In addition, modified mathematical equation to evaluate consequences of risk which allow more generalization and accuracy of weighing the possible losses (performance, financial, ecology and human) is developed. The results demonstrate an improvement at the assessment of the probability of risk and provide better understanding of the impact of the risk on the major identified areas within the petroleum industry.

Optimal inspection planning for onshore pipelines subject to external corrosion

Continuous operation of pipeline systems involves significant expenditures in inspection and maintenance activities. The cost-effective safety management of such systems involves allocating the optimal amount of resources to inspection and maintenance activities, in order to control risks (expected costs of failure). In this context, this article addresses the optimal inspection planning for onshore pipelines subject to external corrosion. The investigation addresses a challenging problem of practical relevance, and strives for using the best available models to describe random corrosion growth and the relevant limit state functions. A single pipeline segment is considered in this paper. Expected numbers of failures and repairs are evaluated by Monte Carlo sampling, and a novel procedure is employed to evaluate sensitivities of the objective function with respect to design parameters. This procedure is shown to be accurate and more efficient than finite differences. The optimum inspection interval is found for an example problem, and the robustness of this optimum to the assumed inspection and failure costs is investigated. It is shown that optimum total expected costs found herein are not highly sensitive to the assumed costs of inspection and failure.

Optimizing inspection intervals—Reliability and availability in terms of a cost model: A case study on railway carriers

This paper states the problem that railway carriers have with inspections and maintenance in its most cost optimal way. Often there is external pressure to improve reliability and availability and to reduce costs significantly. To overcome this problem this paper suggests a model that tries to find the optimal inspection interval. Not all maintenance companies have their inspection intervals that match with the actual reliability of a system anymore and the inspection intervals are not necessarily at a cost optimum. This research retrieves the actual failure and repair data and combines this together with the availability of a system to find the optimum inspection interval in terms of costs. The application of the optimization approach to a railway carrier maintenance company in the Netherlands is also presented.

Optimum inspection interval for a system under periodic and opportunistic inspections

This article proposes a model to find an optimal periodic inspection interval over a finite time horizon for a multi-component system. The system’s components are subject to either hard or soft failures. Hard failures are detected and fixed instantaneously. Soft failures are unrevealed and can only be detected at inspections. Soft failures do not stop the system from operating; however, they may reduce its level of performance from its designed value. The system is inspected periodically to detect soft failures; however, a hard failure instance also provides an opportunity called opportunistic inspection to inspect and fix soft failures. Two models are discussed in this article. The first model assumes that components with soft and hard failures are minimally repaired. The second model assumes the possibility of either minimal repair or replacement of a component with soft failure, with some age-dependent probabilities. Recursive procedures are developed to calculate the expected number of minimal repairs and replacements and expected downtimes of components with soft failure. Examples of the calculation of the optimal inspection intervals are given. The data used in the examples are adapted from a hospital’s maintenance data for general infusion pump.

Optimisation of Wind Turbine Inspection Intervals

The choice of correct inspection intervals poses a serious challenge to industries that utilise physical assets. Too short an interval increases operational cost and waste production time while too long an interval increases the likelihood of unexpected asset failures. Failure Modes and Effect Criticality Analysis (FMECA) is a technique that permits qualitative evaluation of assets' functions to predict critical failure modes and the resultant consequences to determine appropriate maintenance tasks for the assets. The Delay-Time Maintenance Model (DTMM) is a quantitative maintenance optimisation technique that examines equipment failure patterns by taking into account failure consequences, inspection time and cost in order to determine optimum inspection interval. In this paper, a hybrid of FMECA and DTMM is used to assess the failure characteristics of a selected subsystems of a chosen wind turbine. Optimal inspection intervals for critical subsystems of the wind turbine are determined to minimise its total life-cycle cost.

A stochastic approach to determine lifetimes and inspection schemes for aircraft components

This paper describes a stochastic methodology for predicting aircraft component fatigue lifetimes and optimum inspection intervals. An introduction covers some basics in design philosophies and the main aspects of deterministic and stochastic analysis. Then a stochastic life approach (SLAP) is presented. This consists of three steps: (1) determining the failure distribution; (2) reverse crack growth analyses from the failure distribution to obtain the initial inspection time and corresponding crack length distribution; and (3) forward crack growth analyses to determine a schedule for repeated inspections. The SLAP methodology is illustrated using in-service inspection data from an actual aircraft component and comparing the results with a deterministic analysis. Concluding remarks summarise the findings and address open issues.

Wind Turbine Maintenance Optimisation: Principles of Quantitative Maintenance Optimisation

Maintenance optimisation is a crucial issue for industries that utilise physical assets due to its impact on costs, risks and performance. Current quantitative maintenance optimisation techniques include Modelling System Failures MSF (using monte-carlo simulation) and Delay-Time Maintenance Model (DTMM). The MSF investigates equipment failure patterns by using failure distribution, resource availability and spare-holdings to determine optimum maintenance requirements. The DTMM approach examines equipment failure patterns by considering failure consequences, inspection costs and the period to determine optimum inspection intervals. This paper discusses the concept, relevance and applicability of the MSF and DTMM techniques to the wind energy industry. Institutional consideration as well as the benefits of practical implementation of the techniques are highlighted and discussed.

An analysis of Taguchi's on‐line quality monitoring procedure for attributes with diagnosis errors

AbstractThe on‐line quality monitoring procedure for attributes proposed by Taguchi, Elsayed and Hsiang for processes in which the fraction defective shifts from 0 at some point are examined under the assumption of diagnosis errors. The renewal reward (economic) approach of Nayebpour and Woodall is used to develop a mathematical expression for the long‐run expected cost per item produced and assessment of the optimum inspection interval is discussed. Copyright © 2001 John Wiley & Sons, Ltd.

Practical models for condition monitoring inspection intervals

Markov models find optimum inspection intervals for phased deterioration of monitored complex components in a system with severe down time costs. The number of (pseudo)‐phases can be increased, but in most cases, simple models tracking actual states and their perception by the user will suffice, because of paucity of data and near‐constant rates. The matrix is cyclic; it includes renewal and regression to earlier states, simplifying solution and matching observation. An example involves roller bearings in paper mills with three phases, no defect, possible defect, and final deterioration towards failure. In the last phase, continuous monitoring is used.

A delay-time model for repairable machinery: maximum likelihood estimation of optimum inspection intervals

A delay-time model for repairable machinery: maximum likelihood estimation of optimum inspection intervals

Optimum condition-based maintenance policies for deteriorating systems with partial information

Systems are considered which deteriorate as time goes on and whose conditions can be observed. The optimum maintenance policy with respect to cost is determined, based on a continuous deterioration process, by formulating the decision process as a discrete Markov decision problem. Examples from the field of civil engineering are given. The relation between the type of deterioration and condition-based optimum inspection intervals, optimum repair level, minimum average maintenance costs and mean time to repair is shown, using some numerical results.

Response spectrum methods in tank-vehicle design

Existing design rules for tank vehicles have proved insufficient, because vibrations often cause fatigue cracks. Measurements have been performed on the tank to provide a picture of the influence of different road types and filling ratios. Shock response spectrum analysis (SRS) was used to obtain a measure of single-dynamic events. To get a basis for dimensioning against fatigue a calculation of fatigue-damage response spectra (FDRS) has been performed. This relates the vibrations in the tank to the risk of fatigue damage. In lieu of cycle counting by rain-flow count techniques, which has certain disadvantages, a new model, the so called HdM model, for fatigue-life assessment based on level crossing has been used. Further development of the results of the analysis can be used to improve design criteria in transport tank regulations, as well as to determine optimum inspection intervals for, in particular, tanks for hazardous materials.

Can models fitted to small data samples lead to maintenance policies with near-optimum cost?

In a general context of maintenance modelling, the excess cost of operating at the estimated optimum value of some decision variable (as opposed to the true unknown optimum) is related to the sample size of the dataset available for modelling. Although large sample sizes are required to estimate the optimum value of a decision variable to a high accuracy, it is shown that the cost which is optimised can be reduced to near its true minimum value even for small sample sizes. In fact, the expectation of the excess cost is inversely proportional to sample size. This implies that even modest sample sizes would be sufficient for the practical use of sensible models for determining cost-based maintenance policies. An example relating to a simple model for determining the optimum inspection interval for a repairable system illustrates these ideas. In particular, some results on the maintenance of medical equipment are presented.

Revising models of maintenance and inspection

Formal methods of updating delay-time models of industrial inspection practice associated with maintenance are proposed. Delay-time models are derived from subjective estimates and consequently are not guaranteed to model the status quo, i.e., the observed failure characteristics and downtime, say of a system under an existing inspection practice. The existence and uniqueness of solutions when updating the prior distribution of delay-time using a linear transform are discussed. The scope for updating the prior model through modifying the perceived degree of perfectness of inspection is also addressed. Criteria for selecting an updating procedure in the case where options are not unique are proposed, and the decision consequences of the change in the optimum inspection interval commented on. A case study is used to highlight the problem and to demonstrate the methods suggested.

Practical availability analysis of standby systems : Jussi K. Vaurio. IEEE Proc. Reliab., 125 (1982)

Comprehensive availability models are developed in this paper for analyzing redundant standby safety systems. The models, which include both periodic inspections and continuous monitoring for discovering failures, incorporate human errors associated with testing, monitoring, and repair as well as spurious alarms and failures after a demand during the mission time. Equations are developed for calculating optimum inspection intervals and average steady state availabilities of standby m-out-of-n:G systems (with several schemes of staggering the tests), as are techniques for including undetected and common-cause failures. Analytical models provide a basis for many generic conclusions. Numerical examples representative of safety systems in nuclear power plants illustrate, for example, the importance of developing monitoring techniques to detect errors made in testing and returning components to service after maintenance.

A preventive maintenance plan for a system subject to revealed and unrevealed faults

A system consists of two modules, both of which must operate for the system to operate. One of the modules is inaccessible to inspection, while the second is continuously monitored. When a revealed fault occurs in the second module the system is replaced by a new system. So if no preventive maintenance plan is used then the system will fail to operate when an unrevealed fault occurs in the first module until a revealed fault occurs in the second module and the system is replaced. A preventive maintenance plan can be used to periodically replace the system with a new system, and so improve the availability of the systems. The optimum inspection intervals can be chosen using either the criterion of maintaining the system availability above a given level or the criterion of minimising the cost of the expected number of unmet demands on the system and the cost of the plan. Results are obtained for a model of the system when replacement is instantaneous. Firstly, these are given when the modules fail independently and the failure times have negative exponential distributions. Secondly, these are given when the modules' failures are dependent. In this case it can be shown that if the failure times to unrevealed faults (in the first module) have a negative exponential distribution and this distribution is independent of the distribution of times between unrevealed faults (in the first module) and revealed faults (in the second module) then the results are independent of the distribution of failure times to revealed faults (in the second module). The optimum inspection intervals are given for these models.