Condition-based Maintenance Optimization for Gearbox Using Proportional Hazards Model

Optimal condition-based maintenance decisions for systems with dependent stochastic degradation of components

Condition-based maintenance (CBM) is proved to be effective in reducing the long-run operational cost for a system subject to degradation failure. Most existing research on CBM focuses on single-unit systems where the whole system is treated as a black box. However, a system usually consists of a number of components and each component has its failure behavior. When degradation of the components is observable, CBM can be applied to the component level to improve the maintenance efficiency. This paper aims to study the optimal inspection/replacement CBM strategy for a multi-unit system. Degradation of each component is assumed to follow a Wiener process and periodic inspection is considered. We cast the problem into a Markov decision framework and derive the optimal maintenance decisions that minimize the maintenance cost. To better illustrate the optimal maintenance strategy, we start from a 1-out-of-2: G system and show that the optimal maintenance policy is a two-dimensional control limit policy. The argument used in the 1-out-of-2: G system can be readily extended to general cases in a similar way. The value iteration algorithm is used to find the optimal control limits, and the optimal inspection interval is subsequently determined through a one-dimensional search. A numerical study and a comprehensive sensitivity analysis are provided to illustrate the optimal maintenance strategy.

Recent heightened enthusiasm towards Industrial Artificial Intelligence (IAI) and Industrial Internet of Things (IIoT) coupled with developments in smart sensor technologies have resulted in simultaneous incorporation of several advanced Condition Monitoring (CM) technologies within manufacturing and industrial sectors. Efficient utilization of CM data leads to enhanced safety, reliability and availability of manufacturing systems. In this regard, the paper proposes an efficient and novel hybrid Maintenance Decision Support System (MDSS) for fault diagnostic and prognostic considering CM data along with event- triggered data. The proposed MDSS model is a hybrid Machine Learning (ML)-based solution coupled with statistical techniques. In order to find an optimal maintenance policy, we concentrate the attention on a time-dependent Proportional Hazards Model (PHM) augmented with a semi-supervised ML approach. The developed hybrid model is capable of inferring and fusing High-Dimensional and Multi-modal Streaming (HDMS) data sources in an adaptive and autonomous fashion to recommend optimal maintenance decisions without human intervention. To illustrate the complete structure of the proposed MDSS, experimental evaluations are designed based on a dataset provided by NASA containing run-to-failure and CM data associated with aircraft engines. The effectiveness of the proposed model is demonstrated through a comprehensive set of comparisons with different ML algorithms.

Joint optimization of maintenance and spares inventory policy for a series-parallel system considering dependent failure processes

Condition based maintenance (CBM) is based on collecting observations over time, in order to assess equipment's state, to prevent its failure and to determine the optimal maintenance strategies. In this paper, we derive an optimal CBM replacement policy when the state of equipment is unknown but can be estimated based on observed condition. We use a proportional hazards model (PHM) to represent the system's degradation. Since equipment's state is unknown, the optimization of the optimal maintenance policy is formulated as a partially observed Markov decision process (POMDP), and the problem is solved using dynamic programming. Practical advantages of combining the PHM with the POMDP are shown.

Maintenance decision-making has emerged as an important area of industrial research. Over the past two decades, maintenance policies have evolved from simple reactive maintenance to complex versions of condition-based maintenance (CBM). A quantitative description of a machine’s health, as found in CBM, is essential to plan maintenance effectively as it helps avoid excessive or insufficient maintenance. In spite of several advancements in the degradation monitoring techniques, most CBM decision-making methods still focus on a single machine system. Maintenance analysis of a single machine provides good insights, but lacks practical applications. In this paper, we develop a continuous time Markov chain degradation model and a cost model to quantify the effects of maintenance on a multiple machine system. An optimal maintenance policy for a multiple machine system in the absence of resource constraints is obtained. In the presence of resource constraints, two prioritization methods are proposed to obtain effective maintenance policies for a multiple machine system. A case study focusing on a section of an automotive assembly line is used to illustrate the effectiveness of the proposed method.

Optimal maintenance policy and residual life estimation for a slowly degrading system subject to condition monitoring

Condition based maintenance optimization for multi-component systems using proportional hazards model

User costs are usually not included when optimal maintenance strategies and decisions are made, although authors often mention that user costs ought to be included. The life-cycle costs are minimized for the considered structure without considering the often significant costs for the users of the bridge and even without considering the long-term effects of the decision. Unfortunately, the maintenance decisions are often political decisions which are not easy to accept for the community. There is clearly a need to convince the decision-makers that user costs should be considered when major decisions are made.

As a maintenance strategy to reduce unexpected failures and enable safe operation, condition-based maintenance (CBM) has been widely used in recent years. The maintenance decision criteria of CBM in the literature mostly originate from statistical failure data or degradation states, few of which can directly and effectively reflect the current state and analyze condition monitoring data, maintenance measures, and reliability together at the same time. In this paper, we introduce the performance margin as a decision criterion of CBM. We propose a condition-based maintenance optimization method using performance margin. Considering a CBM optimization problem for a degrading and periodically inspected component, a newly developed performance margin degradation model is established when three different maintenance measures become involved. Maintenance measure effect factors, maintenance decision vectors, and maintenance measure threshold vectors are developed to update the degradation model. And to build a maintenance optimization model, both cost and loss related to maintenance decision problems and reliability obtained by performance margin have been taken into consideration. Finally, a numerical example is provided to illustrate the proposed optimization method.

Joint optimization of budget allocation and maintenance planning of multi-facility transportation infrastructure systems

Joint optimization of condition-based maintenance and inventory control for a k-out-of-n:F system of multi-state degrading components

In many applications, units from the same population exhibit heterogeneity that they degrade with different rates due to random factors. This article studies how this heterogeneity in degradation influences condition-based maintenance (CBM) policy. Many CBM polices are developed based on gamma process because it is popularly used to characterise monotone degradation processes. In this study, we also model the unit’s degradation by gamma process. To account for the heterogeneity among units’ degradation, we incorporate a random effect parameter in the gamma process. Then the optimal policy for CBM is obtained through Markov decision process. We show that when heterogeneity exists, the transition probability of degradation state depends on both unit’s age and observed degradation level. And consequently, the optimal maintenance policy is a monotone control limit policy. We conduct extensive numerical experiments to validate and demonstrate our findings in depth.

Maintenance can be the factor of either increasing or decreasing system's availability, so it is valuable work to evaluate a maintenance policy from cost and availability point of view, simultaneously and according to decision maker's priorities. This study proposes a Partially Observable Markov Decision Process (POMDP) framework for a partially observable and stochastically deteriorating system in which inspection and maintenance optimal policies of Condition Based Maintenance (CBM) must be determined to maximize the average profit and availability of the system simultaneously. A recent exact method named Accelerated Vector Pruning method (AVP) and some other popular estimating and exact methods are applied and compared in solving such problems.

This study proposes a sequential Condition-Based Maintenance (CBM) policy for intelligent monitored system based on cost and reliability prioritisation. It is assumed that system's reliability could be continuously monitored, whenever it reaches the threshold R, and scheduled maintenance activity is performed to restore the system. This maintenance policy differs from other policies in taking into consideration of influences from the frequency of maintenance activities and operating time on system's failure rate function subject to a deterioration process. Meanwhile, this CBM policy firstly considers the variant operational cost and variant minimal repair cost by the effects of system's corresponding condition, which helps to construct more practical maintenance model. Finally, a numerical example is presented to test this proposed sequential predictive maintenance policy and the simulation results show its efficiency.