Four types of maintenance decisions are taken on a cable asset: “no action” NA, “preventive maintenance” PM, “replacement” RP, and “corrective maintenance” CM. (2006). presented two system-level RCM optimization methods (Yssaad et al. Res. 2. . The optimal decision policy depends on four types of cost: The cost of replacement \( (C_{\text{RP}} ) \) of a power cable in a distribution network is as follows: In Eq. \\ \end{array} } \\ \end{array} } \right. Change ), You are commenting using your Facebook account. In second part, for each stage, the algorithm finds the minimum cost of a maintenance action for all the cable states. Recursion and dynamic programming (DP) are very depended terms. Power cables play an integral part in the transmission and distribution of electricity. The optimal maintenance policy was found for two maintenance time period to show the outcome of the model for time period before the end of life and until the end of expected lifetime. It shows how to use the Figaro language to build a spam filter and apply Bayesian and Markov networks to diagnose … The implementation of maintenance activity depends on the past failure causes. Change ), You are commenting using your Google account. 2015a). Key Idea. Objective Obtain optimal maintenance policy that minimizes the total maintenance cost over a finite planning horizon \( 0 < y < Y \). In: IEEE 11th International conference on the properties and applications of dielectric materials (ICPADM), Sydney, pp 380–383, Sachan S, Zhou C, Bevan G, Alkali B (2015c) Prediction of power cable failure rate based on failure history and operational conditions. It can be assumed that the failure probability reduces by the same percentage and this affects the relative age of cable in comparison to cables without maintenance (Bertling et al. Here, NA means take no maintenance action on cables. Dynamic programming turns out to be an ideal tool for dealing with the theoretical issues this raises. The decision of corrective maintenance is not take at the final stage (\( y = Y \)) of planning horizon. A Dynamic Programming Algorithm for Inference in Recursive Probabilistic Programs. The probability of failure and XLPE insulation degradation level is shown in Fig. 2016). Manufacturing techniques, material, design, and installation method improve within a few years of time frame (Orton 2013, 2015). What is High Quality Programming Code What is the best programming language that can be used in an introductory level course for computer programming concepts and software development? In this model, the length of the planning horizon is equivalent to the expected lifetime of the cable. Mathematics, Computer Science We present a data-driven, probabilistic trajectory optimization framework for systems with unknown dynamics, called Probabilistic Differential Dynamic Programming (PDDP). PDDP takes into account uncertainty explicitly for dynamics models using Gaussian processes (GPs). It could restore a cable to “good as new” state with effective age equal to 1, “worse than before” state \( a^{'} > a \), and “bad as old” state, \( a^{'} = a \). Second, repair when corrective maintenance (\( {\text{CM}} \)) is carried out on failed cable when the failure cause remains undetected and eventually fails in the future. PDDP takes into account uncertainty explicitly for … Minimum maintenance cost incurs due to decisions at the end of planning horizon at stage \( y = Y \) for state \( a^{'} \) (effective age) is zero, \( F \) (fail) is replacement cost, and \( a^{'} = A^{'} \) is both failure and replacement cost shown in Eq. (4) \( \bar{F}_{\text{PM}} \) = 0.60 \( \times \) 0.98 and \( F_{\text{PM}} \) = 0.40 \( + \) 0.60 \( \times \) 0.02]. The degradation level and planning horizon of cable population installed in year \( i_{0} \) and \( i_{1} \) is shown in Fig. The tree on the righthand-side has a lowest cost path of value and the lefthand-side tree has lowest cost and the edges leading to each, respective tree, have costs and . The annual maintenance cost per \( {\text{km}} \) is \( (C_{\text{PM}} ) \): In Eq. Background We start this section with some examples to familiarize the reader with probabilistic programs, and also informally explain … The model can be used by power utility managers and regulators to assess the financial risk and schedule maintenance. High Volt Eng 41(4):1178–1187, Sachan S, Zhou C, Bevan G, Alkali B (2015b) Failure prediction of power cables using failure history and operational conditions. $$, $$ {\text{CM}}:\left\{ {\begin{array}{*{20}c} {F_{\text{CM}} : P\left( {F_{{a_{y + 1 }^{'} }} |F_{{a_{y }^{'} }} ,{\text{CM}}} \right) = 1 - P\left( {a_{y }^{'} |F_{{a_{y }^{'} }} ,{\text{CM}}} \right)} \\ {\begin{array}{*{20}l} { } \\ {\bar{F}_{\text{CM}} : P\left( {a_{y + 1 }^{'} |F_{{a_{y }^{'} }} ,{\text{CM}}} \right) = P\left( {a_{y }^{'} |F_{{a_{y }^{'} }} ,{\text{CM}}} \right).} Matrix\( R_{y} \) For each planning stage y, the result is stored in matrixes which have two columns and rows equal to the number of expected states at any stage y of the planning horizon. In this example, the year 2016 is considered as the current year and optimal maintenance plan is launched from this year. Minimum maintenance cost at stage \( y \) of planning horizon is \( V_{y} \) and expected future cost of maintenance at stage \( y + 1 \) is \( V_{y + 1} \). Manufacturers conduct quality tests on each cable section to detect the expected fault. This is done by defining a sequence of value functions V1, V2,..., Vn taking y as an argument representing the state of the system at times i from 1 to n. This is called the Plant Equation. The probabilistic case, where there is a probability dis- tribution for what the next state will be, is discussed in the next section. The degradation can be quantified in terms of percentage with the advancement of age for a group of cable with similar installation year, design, and operational conditions. Further, let (Resp. ) Definition. Transition probability of preventive maintenance PM decision is obtained by assuming that only 60% (0.60) of potential failure causes can be detected (DET) and rest 40% (0.40) remain undetected (U_DET), and there is 0.98 and 0.02 chance that PM action would be successful and unsuccessful, respectively. World Acad Sci Eng Technol 53:636–639, Lassila J, Honkapuro S, Partanen J (2005) Economic analysis of outage costs parameters and their implications on investment decisions. The relevance of mathematical developments in dynamic programming and Bayesian statistics to dynamic decision theory is examined. We now give a general definition of a dynamic programming: Time is discrete ; is the state at time ; is the action at time ; The state evolves according to functions . The PM repair cost depends on the type of preventive maintenance action taken on the detected potential failure location. knowledge of dynamic programming is assumed and only a moderate familiarity with probability— including the use of conditional expecta-tion—is necessary. An application of dynamic programming for maintenance of power cable was presented by Bloom et al. Specifically, once we reach the penultimate node on the left (in the dashed box) then it is clearly optimal to go left with a cost of . Recursively define the value of the solution by expressing it in terms of optimal solutions for smaller sub-problems. Effect of maintenance on the cable must be quantified appropriately to make an effective maintenance plan. 2014; Yssaad and Abene 2015). This paper presents a probabilistic dynamic programming algorithm to obtain the optimal cost-effective maintenance policy for a power cable. For ex. In dynamic Programming all the subproblems are solved even those which are not needed, but in recursion only required subproblem are solved. Kolmogorov’s axioms of probability The probability P(A) of an event A is a nonnegative real number. The severe degradation in entire insulation and high maintenance cost compared to replacement cost is justifiable a reason to support the proactive replacement of the unjacketed cables between the years 2030–2034 (\( y = 14 \)–\( 18 \)). In combinatorics, C(n.m) = C(n-1,m) + C(n-1,m-1). The random failure behaviour of the power cable is included in the model by considering it as a stochastic or random process. Security Optimization of Dynamic Networks with Probabilistic Graph Modeling and Linear Programming Hussain M.J. Almohri, Member, IEEE, Layne T. Watson Fellow, IEEE, Danfeng (Daphne) Yao, Member, IEEE and Xinming Ou, Member, IEEE Abstract— Securing the networks of large organizations is technically challenging due to the complex configurations and constraints. There are a number of ways to solve this, such as enumerating all paths. In this paper, probabilistic dynamic programming algorithm is proposed to obtain optimal cost-effective maintenance policy for power cables in each stage (or year) of the planning period. The aim of a probabilistic logic (also probability logic and probabilistic reasoning) is to combine the capacity of probability theory to handle uncertainty with the capacity of deductive logic to exploit structure of formal argument. "Dynamic Programming may be viewed as a general method aimed at solving multistage optimization problems. Probabilistic Parser Implementations. $$, $$ {\text{for}}\, y = 0\,{\text{to}}\,Y - 1 $$, $$ V_{y} \left( {a^{'} } \right) = \hbox{min} \left( {\begin{array}{*{20}c} {\begin{array}{*{20}c} {{\text{NA:}} \,\bar{F}_{\text{NA}} V_{y + 1} \left( {a_{y + 1 }^{'} } \right) + F_{\text{NA}} V_{y + 1} \left( {F_{{a_{y + 1 }^{'} }} } \right)} \\ {{\text{PM:}}\, C_{\text{PM}} + C_{{{\text{RE}}_{\text{PM}} }} + \bar{F}_{\text{PM}} V_{y + 1} \left( {a_{y + 1 }^{'} } \right) + F_{\text{PM}} V_{y + 1} \left( {F_{{a_{y + 1 }^{'} }} } \right)} \\ \end{array} } \\ {{\text{RP:}}\, C_{\text{RP}} + \bar{F}_{\text{RP}} V_{y + 1} \left( 1 \right) + F_{\text{RP}} V_{y + 1} \left( {F_{{a_{y + 1 }^{'} }} } \right)} \\ \end{array} } \right), $$, $$ V_{y} \left( F \right) = \hbox{min} \Bigg( {{\text{CM:}} \,C_{F} + C_{{{\text{RE}}_{\text{CM}} }} + \bar{F}_{\text{CM}} V_{y + 1} \left( {a_{y}^{'} } \right) + F_{\text{CM}} V_{y + 1} \left( {F_{{a_{y + 1 }^{'} }} } \right)} \Bigg) . This has engendered a demand for high reliability and a need for the extension of cable life with minimum maintenance cost which can only be achieved by implementation of an effective maintenance policy. Cost of corrective or preventive failure is much less than completes replacement. 4. We report on a probabilistic dynamic programming formulation that was designed specifically for scenarios of the type described. Therefore, there are two possible kinds of repair. Viewed 2k times 0. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. An Introductory Example. The idea of solving a problem from back to front and the idea of iterating on the above equation to solve an optimisation problem lies at the heart of dynamic programming. The cost of maintenance decisions at effective age \( (a^{'} \)) and fail (\( F) \) state for stage \( y = 0 \,{\text{to}}\,Y - 1 \) is shown in Eqs. It was estimated that, by the years 2030 and 2055, the entire insulation of the cable is expected to reach 75% of moderately severe and 99.8% of severe of insulation degrade level, respectively (can be seen in Fig. If for example, we are in the intersection corresponding to the highlighted box in Fig. . Optimisation problems seek the maximum or minimum solution. We describe a dynamic programming algorithm for computing the marginal distribution of discrete probabilistic programs. This makes probabilistic programs attractive for scientific research: when hypotheses are formalized as programs, it is possible to quickly explore the space of hypotheses. Maintenance activity such as preventive maintenance (PM) action reduces the failure probability; however, the PM methods can only detect some potential failure causes and other causes remain undetected. Bigger problems the value of the entire electrical distribution network degradation level, and do nothing as a.... 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