Evaluation of reliability of quickly repairable components of transshipment machines and mechanisms

When calculating the reliability indicators of transshipment machines and mechanisms, the required values are not always obtained explicitly due to systematic difficulties (complex distribution laws) or limited initial data. The optimal stock of spare parts for transshipment machines and mechanisms depends on the reliability indicators, and the nature and intensity of their use. Downtime due to the failure of transshipment machines and mechanisms used for ship handling leads to greater losses than downtime of these machines in the warehouse. Failures of transshipment machines and mechanisms can be due to the failure of non-repairable and quickly repairable components, and those with a long repair time. To calculate the main reliability indicators of quickly repairable components, the most common laws of distribution of random variables were employed. The paper considers the methodological aspects of the probabilistic reliability estimate of quickly repairable components of transshipment machines and mechanisms.

Method for testing modern common rail piezoelectric fuel injectors

The paper presents the author's own method for testing piezoelectric common rail fuel injectors, which for many years were considered non-repairable components. This was mainly due to the lack of availability of spare parts and dedicated measuring equipment, enabling full diagnostics under test bench conditions. As a result, their workshop and laboratory servicing was very limited, as effective disassembly concerned basicaly only the plunger and barrel assembly (needle with nozzle) for selected reference models. The situation has now improved to such an extent that an author’s own regeneration procedure has been proposed with the replacement of the most important controls and actuators. The tests were carried out on the example of Siemens VDO Continental PCR 2.3 fuel injectors from one engine, listing the most important stages of this process, including the correction of fuel dosage and returns.

A Demand Prediction Model of Repairable Components based on Grey Birth and Death Process

During the life cycle of equipment, the failure and repair rates of repairable components show uncertain characteristics. The birth and death process (BDP) based on the determined failure and repair rates may not meet the demand forecasting of spare parts. In order to resolve this problem, the grey state transition matrix is constructed by using interval grey numbers to appropriately represent the failure and repair rates of repairable components. In addition, the grey BDP model is built for the demand forecasting of spare parts. The memoryless and existence conditions of steady solution of the grey BDP are studied. To some extent, the spare parts demand law with the uncertain information of the failure and repair rates can easily be revealed. The practical case study is provided to verify the validity and practicability of the proposed model. Also, it provides a new perspective for the spare parts demand prediction problem under the condition of uncertain Markov Process. Accordingly, airlines can predict the maintenance resources demand more accurately and avoid two situations which are not allowed: (1) lower spare parts inventory will lead to the delay production; and (2) higher spare parts inventory will lead to the operating cost pressure.

Optimal Selective Maintenance Decision-Making for Consecutive-Mission Systems with Variable Durations and Limited Maintenance Time

Maintenance is inevitable for repairable components or systems in modern industries. Since the maintenance effectiveness has a great influence on the subsequent operations and in addition, different maintenance options are possible for the components of the system during the break between any two successive missions, the optimal selective maintenance strategy needs to be determined for a system performing successive missions. A number of selective maintenance models were set up on the basis that the durations of each mission are predetermined, the maintenance time is negligible, and the states of the components at the end of the previous mission can be accurately obtained. However, in the actual industrial and military missions, these premises may not always hold. In this paper, a novel selective maintenance model under uncertainties and limited maintenance time is proposed to improve these deficiencies. The genetic algorithm is selected to solve the optimization problem, and an illustrative example is presented to demonstrate the proposed method. The optimal selective maintenance decision without the constraint of maintenance time is used for comparison.

Optimization of the replacement time for critical repairable components

Equipment-intensive industries must manage critical components due to their impact on the availability and high inventory carrying costs. In this context, this study seeks to assess mean times between interventions (MTBI) and mean times between failures (MTBF) to determine optimal replacement times for critical repairable components used in six EX5500 hydraulic excavators operating at an open-pit mining site. For these purposes, the authors compared a base policy using the MTBF values provided by the equipment manufacturer, against the proposed policy using the MTBI values obtained from equipment intervention records. The results from the study, revealed that the MTBI policy was able to streamline the replacement times for critical repairable components, thus, generating a cost optimization model at a higher level of reliability

BIRNBAUM CRITICALITY AND IMPORTANCE MEASURES FOR MULTISTATE SYSTEMS WITH REPAIRABLE COMPONENTS

We suggest four new measures of importance for repairable multistate systems based on the classical Birnbaum measure. Periodic component life cycles and general semi-Markov processes are considered. Similar to the Birnbaum measure, the proposed measures are generic in the sense that they only depend on the probabilistic properties of the components and the system structure. The multistate system model encodes physical properties of the components and the system directly into the structure function. As a result, calculating importance is easy, especially in the asymptotic case. Moreover, the proposed measures are composite measures, combining importance for all component states into a unified quantity. This simplifies ranking of the components with respect to importance. The proposed measures can be characterized with respect to two features: forward-looking versus backward-looking and with respect to how criticality is measured. Forward-looking importance measures focus on the next component states, while backward-looking importance measures focus on the previous component states. Two approaches to measuring criticality are considered: probability of criticality versus expected impact. Examples show that the different importance measures may result in unequal rankings.

A Framework for Underground Gas Storage System Reliability Assessment Considering Functional Failure of Repairable Components

As one of the most important means of nature gas peak shaving and energy strategic reserving, the reliability assessment of underground gas storage (UGS) system is necessary. Although many methods have been proposed for system reliability assessment, the functional heterogeneity of components and the influence of hydrothermal parameters on system reliability are neglected. To overcome these problems, we propose and apply a framework to assess UGS system reliability. Combining two-layer Monte Carlo simulation (MCS) technique with hydrothermal calculation, the framework integrates dynamic functional reliability of components into system reliability evaluation. To reflect the state transition process of repairable components and their impact on system reliability, the Markov model is introduced at system level. In order to improve the calculation speed, artificial neural network (ANN) model based on off-line MCS is established to replace the on-line MCS at components level. The proposed framework is applied to the reliability assessment and operation optimization of an UGS under different operation conditions. Compared with the traditional single-layer MCS method, the proposed method can not only reflect the variation of UGS reliability with hydrothermal parameters and operation time, but also can improve evaluation efficiency significantly.