Data-driven machine criticality assessment – maintenance decision support for increased productivity

Data-driven decision support for maintenance management is necessary for modern digitalized production systems. The data-driven approach enables analyzing the dynamic production system in realtime. Common problems within maintenance management are that maintenance decisions are experience-driven, narrow-focussed and static. Specifically, machine criticality assessment is a tool that is used in manufacturing companies to plan and prioritize maintenance activities. The maintenance problems are well exemplified by this tool in industrial practice. The tool is not trustworthy, seldomupdated and focuses on individual machines. Therefore, this paper aims at the development and validation of a framework for a data-driven machine criticality assessment tool. The tool supports prioritization and planning of maintenance decisions with a clear goal of increasing productivity. Four empirical cases were studied by employing a multiple case study methodology. The framework provides guidelines for maintenance decision-making by combining the Manufacturing Execution System (MES) and Computerized Maintenance Management System (CMMS) data with a systems perspective. The results show that by employing data-driven decision support within the maintenance organization, it can truly enable modern digitalized production systems to achieve higher levels of productivity

Predictive Maintenance on the Machining Process and Machine Tool

This paper presents the process required to implement a data driven Predictive Maintenance (PdM) not only in the machine decision making, but also in data acquisition and processing. A short review of the different approaches and techniques in maintenance is given. The main contribution of this paper is a solution for the predictive maintenance problem in a real machining process. Several steps are needed to reach the solution, which are carefully explained. The obtained results show that the Preventive Maintenance (PM), which was carried out in a real machining process, could be changed into a PdM approach. A decision making application was developed to provide a visual analysis of the Remaining Useful Life (RUL) of the machining tool. This work is a proof of concept of the methodology presented in one process, but replicable for most of the process for serial productions of pieces

Modelling rail track deterioration and maintenance: current practices and future needs

As commercialisation and privatisation of railway systems reach the political agendas in a number of countries, including Australia, the separation of infrastructure from operating business dictates that track costs need to be shared on an equitable basis. There is also a world-wide trend towards increased pressures on rail track infrastructure through increases in axle loads and train speeds. Such productivity and customer service driven pressures inevitably lead to reductions in the life of track components and increases in track maintenance costs. This paper provides a state-of-the-art review of track degradation modeling, as well as an overview of track maintenance decision support systems currently in use in North America and Europe. The essential elements of a maintenance optimisation model currently under development are also highlighted

A metric for assessing and optimizing data-driven prognostic algorithms for predictive maintenance

Prognostic Health Management aims to predict the Remaining Useful Life (RUL) of degrading components/systems utilizing monitoring data. These RUL predictions form the basis for optimizing maintenance planning in a Predictive Maintenance (PdM) paradigm. We here propose a metric for assessing data-driven prognostic algorithms based on their impact on downstream PdM decisions. The metric is defined in association with a decision setting and a corresponding PdM policy. We consider two typical PdM decision settings, namely component ordering and/or replacement planning, for which we investigate and improve PdM policies that are commonly utilized in the literature. All policies are evaluated via the data-driven estimation of the long-run expected maintenance cost per unit time, relying on available monitoring data from run-to-failure experiments. The policy evaluation enables the estimation of the proposed metric. The latter can further serve as an objective function for optimizing heuristic PdM policies or algorithms' hyperparameters. The effect of different PdM policies on the metric is initially investigated through a theoretical numerical example. Subsequently, we employ four data-driven prognostic algorithms on a simulated turbofan engine degradation problem, and investigate the joint effect of prognostic algorithm and PdM policy on the metric, resulting in a decision-oriented performance assessment of these algorithms

A data-driven prognostic model using time series prediction techniques in maintenance decision making

In recent years, current maintenance strategies have extensively evolved in condition-based maintenance solution in order to achieve a near-zero downtime of equipment function. One of these support elements is the use of prognostic. Prognostic has progressed as a specific function over for the last few years. It provides failure prediction and remaining useful lifetime (RUL) estimation of a targeted equipment or component. This estimation is beneficial for production or maintenance people as it allows them to focus on proactive rather than reactive action. While some prognostic models are created based on the historical failure data, others remain as simulation models serving as a pre-exposure effect analysis. Although the concept of a data-driven prognostics model using condition monitoring information has been widely proposed, the validation in predicting the target value continues to be a challenge. In addition, the prognostics have not been applied directly within the maintenance decision making. Hence, the aim of this study is to design a data driven prognostics model that predicts the series of future equipment condition iteratively and allows the process of maintenance decision making to be carried out. The initial phase of this research deals with a conceptual design of data-driven prognostics model. This conceptual design leads to the formulation of a generic data acquisition and time series prediction techniques, which are the key elements to predictive prognostic solution. In this case, there are four techniques have been used and formulated to have better prognostic results namely: Double Exponential Smoothing (DES), Neural Network (NN), Hybrid DES-NN and Enhanced Double Exponential Smoothing (EDES). The intermediate phase of this research involves the development of a computational tool based on the proposed conceptual model. This tool is used for model implementation that uses the experimental data to test the ability of the prognostics model for failure prediction and RUL estimation. It also demonstrates the integration of prognostics model in maintenance decision making. The final phase of this research demonstrates the implementation of the model using industry data. In this phase, the industrial implementation takes into account the performance accuracy to verify the operational framework. The results from the model implementations have shown that the proposed prognostic model can generate the degradation index from the data acquisition, and the formulated EDES can predict RUL estimation consistently. By integrating it with the maintenance cost model, the proposed prognostic model also can perform time–to-maintenance decision. However, the accuracy of the prognostic and maintenance results can be increased with a huge and quality data. In conclusion, this research contributes to the development of data-driven prognostics model based on condition monitoring information using time series prediction techniques to support maintenance decision

District heating network maintenance planning optimization

To ensure the correct functioning of district heating networks and minimize critical failures, utilities allocate every year a significant part of their budget to maintenance operations. In the present work we describe a risk-based approach implemented to tackle the problem of designing optimal multi-year maintenance campaigns, applied to the Italian city of Brescia, showing how data-driven techniques can help decision makers assess the long terms impacts of budget allocations

Predictive maintenance for industry 5.0:behavioural inquiries from a work system perspective

Predictive Maintenance (PdM) solutions assist decision-makers by predicting equipment health and scheduling maintenance actions, but their implementation in industry remains problematic. Specifically, prior research repeatedly indicates that decision-makers often refuse to adopt the data-driven, system-generated advice in their working procedures. In this paper, we address these acceptance issues by studying how PdM implementation changes the nature of decision-makers’ work and how these changes affect their acceptance of PdM systems. We build on the human-centric Smith-Carayon Work System model to synthesise literature from research areas where system acceptance has been explored in more detail. Consequently, we expand the maintenance literature by investigating the human-, task-, and organisational characteristics of PdM implementation. Following the literature review, we distil ten propositions regarding decision-making behaviour in PdM settings. Next, we verify each proposition’s relevance through in-depth interviews with experts from both academia and industry. Based on the propositions and interviews, we identify four factors that facilitate PdM adoption: trust between decision-maker and model (maker), control in the decision-making process, availability of sufficient cognitive resources, and proper organisational allocation of decision-making. Our results contribute to a fundamental understanding of acceptance behaviour in a PdM context and provide recommendations to increase the effectiveness of PdM implementations.</p