Optimisation of Maintenance Policies Based on Right-Censored Failure Data Using a Semi-Markovian Approach

This paper exposes the existing problems for optimal industrial preventive maintenance intervals when decisions are made with right-censored data obtained from a network of sensors or other sources. A methodology based on the use of the z transform and a semi-Markovian approach is presented to solve these problems and obtain a much more consistent mathematical solution. This methodology is applied to a real case study of the maintenance of large marine engines of vessels dedicated to coastal surveillance in Spain to illustrate its usefulness. It is shown that the use of right-censored failure data significantly decreases the value of the optimal preventive interval calculated by the model. In addition, that optimal preventive interval increases as we consider older failure data. In sum, applying the proposed methodology, the maintenance manager can modify the preventive maintenance interval, obtaining a noticeable economic improvement. The results obtained are relevant, regardless of the number of data considered, provided that data are available with a duration of at least 75% of the value of the preventive interval.Ministerio de Ciencia, Innovación y Universidades (MICINN). España RTI2018-094614-B-I00 (SMASHING

Optimisation of Maintenance Policies Based on Right-Censored Failure Data Using a Semi-Markovian Approach

This paper exposes the existing problems for optimal industrial preventive maintenance intervals when decisions are made with right-censored data obtained from a network of sensors or other sources. A methodology based on the use of the z transform and a semi-Markovian approach is presented to solve these problems and obtain a much more consistent mathematical solution. This methodology is applied to a real case study of the maintenance of large marine engines of vessels dedicated to coastal surveillance in Spain to illustrate its usefulness. It is shown that the use of right-censored failure data significantly decreases the value of the optimal preventive interval calculated by the model. In addition, that optimal preventive interval increases as we consider older failure data. In sum, applying the proposed methodology, the maintenance manager can modify the preventive maintenance interval, obtaining a noticeable economic improvement. The results obtained are relevant, regardless of the number of data considered, provided that data are available with a duration of at least 75% of the value of the preventive interval.Proyecto RTI2018-094614-B-I00 (SMASHING), and the “Programa Estatal de I+D+i Orientada a los Retos de la Sociedad

AN APPROACH TO RELIABILITY ANALYSIS OF AIRCRAFT SYSTEMS FOR A SMALL DATASET

Data-driven predictive aircraft maintenance approach typically results in lower maintenance costs, avoiding unnecessary preventive maintenance actions and reducing unexpected failures. Information provided by a reliability analysis of aircraft components and systems can improve an existing maintenance strategy and ensure an optimal maintenance task interval. For reliability work, the exponential distribution is typically used; however, this approach requires substantial amounts of data, which often may not be generated by aviation operations. Therefore, this study proposes a method for reliability analysis given a small dataset. Real-life historical data of an aircraft operating in Nigeria validate the proposed approach and prove its applicability

DECISION SUPPORT MODEL IN FAILURE-BASED COMPUTERIZED MAINTENANCE MANAGEMENT SYSTEM FOR SMALL AND MEDIUM INDUSTRIES

Maintenance decision support system is crucial to ensure maintainability and reliability of equipments in production lines. This thesis investigates a few decision support models to aid maintenance management activities in small and medium industries. In order to improve the reliability of resources in production lines, this study introduces a conceptual framework to be used in failure-based maintenance. Maintenance strategies are identified using the Decision-Making Grid model, based on two important factors, including the machines’ downtimes and their frequency of failures. The machines are categorized into three downtime criterions and frequency of failures, which are high, medium and low. This research derived a formula based on maintenance cost, to re-position the machines prior to Decision-Making Grid analysis. Subsequently, the formula on clustering analysis in the Decision-Making Grid model is improved to solve multiple-criteria problem. This research work also introduced a formula to estimate contractor’s response and repair time. The estimates are used as input parameters in the Analytical Hierarchy Process model. The decisions were synthesized using models based on the contractors’ technical skills such as experience in maintenance, skill to diagnose machines and ability to take prompt action during troubleshooting activities. Another important criteria considered in the Analytical Hierarchy Process is the business principles of the contractors, which includes the maintenance quality, tools, equipments and enthusiasm in problem-solving. The raw data collected through observation, interviews and surveys in the case studies to understand some risk factors in small and medium food processing industries. The risk factors are analysed with the Ishikawa Fishbone diagram to reveal delay time in machinery maintenance. The experimental studies are conducted using maintenance records in food processing industries. The Decision Making Grid model can detect the top ten worst production machines on the production lines. The Analytical Hierarchy Process model is used to rank the contractors and their best maintenance practice. This research recommends displaying the results on the production’s indicator boards and implements the strategies on the production shop floor. The proposed models can be used by decision makers to identify maintenance strategies and enhance competitiveness among contractors in failure-based maintenance. The models can be programmed as decision support sub-procedures in computerized maintenance management systems

ISBIS 2016: Meeting on Statistics in Business and Industry

This Book includes the abstracts of the talks presented at the 2016 International Symposium on Business and Industrial Statistics, held at Barcelona, June 8-10, 2016, hosted at the Universitat Politècnica de Catalunya - Barcelona TECH, by the Department of Statistics and Operations Research. The location of the meeting was at ETSEIB Building (Escola Tecnica Superior d'Enginyeria Industrial) at Avda Diagonal 647. The meeting organizers celebrated the continued success of ISBIS and ENBIS society, and the meeting draw together the international community of statisticians, both academics and industry professionals, who share the goal of making statistics the foundation for decision making in business and related applications. The Scientific Program Committee was constituted by: David Banks, Duke University Amílcar Oliveira, DCeT - Universidade Aberta and CEAUL Teresa A. Oliveira, DCeT - Universidade Aberta and CEAUL Nalini Ravishankar, University of Connecticut Xavier Tort Martorell, Universitat Politécnica de Catalunya, Barcelona TECH Martina Vandebroek, KU Leuven Vincenzo Esposito Vinzi, ESSEC Business Schoo

JIDOKA. Integration of Human and AI within Industry 4.0 Cyber Physical Manufacturing Systems

This book is about JIDOKA, a Japanese management technique coined by Toyota that consists of imbuing machines with human intelligence. The purpose of this compilation of research articles is to show industrial leaders innovative cases of digitization of value creation processes that have allowed them to improve their performance in a sustainable way. This book shows several applications of JIDOKA in the quest towards an integration of human and AI within Industry 4.0 Cyber Physical Manufacturing Systems. From the use of artificial intelligence to advanced mathematical models or quantum computing, all paths are valid to advance in the process of human–machine integration

Risk Management for the Future

A large part of academic literature, business literature as well as practices in real life are resting on the assumption that uncertainty and risk does not exist. We all know that this is not true, yet, a whole variety of methods, tools and practices are not attuned to the fact that the future is uncertain and that risks are all around us. However, despite risk management entering the agenda some decades ago, it has introduced risks on its own as illustrated by the financial crisis. Here is a book that goes beyond risk management as it is today and tries to discuss what needs to be improved further. The book also offers some cases

A Bayesian Network Approach for Product Safety Risk Management

A new method for safety risk management and assessment using Bayesian networks is proposed to resolve limitations of existing methods and to ensure that products and systems available on the market are acceptably safe for use. The method is applicable to a wide range of products and systems, ranging from consumer goods through to medical devices, and even complex systems such as aircraft. While methods such as Fault Tree Analysis (FTA) and Failure Mode and Effects Analysis (FMEA) have been used quite effectively in safety assessment for certain classes of critical systems, they have several limitations which are addressed by the proposed Bayesian network (BN) method. In particular, the BN approach enables us to combine multiple sources of knowledge and data to provide quantified, auditable risk estimates at all stages of a product’s life cycle, including especially when there are limited or no testing or operational safety data available. The BN approach also enables us to incorporate different perceptions of risk, including taking account of personal differences in the perceived benefits of the product under assessment. The proposed BN approach provides a means for safety regulators, manufacturers, risk professionals, and even individuals to better assess safety and risk. It is powerful and flexible, can complement traditional safety and risk assessment methods, and is applicable to a far greater range of products and systems. The method can also be used to validate the results of traditional safety and risk assessment methods when relevant data become available. It is demonstrated and validated using case studies from consumer product safety risk assessment and medical device risk management

Hybrid Statistical and Deep Learning Models for Diagnosis and Prognosis in Manufacturing Systems

In today’s highly competitive business environment, every company seeks to work at their full potential to keep up with competitors and stay in the market. Manager and engineers, therefore, constantly try to develop technologies to improve their product quality. Advancements in online sensing technologies and communication networks have reshaped the competitive landscape of manufacturing systems, leading to exponential growth of Condition Monitoring (CM) data. High-dimensional data sources can be particularly important in process monitoring and their efficient utilization can help systems reach high accuracy in fault diagnosis and prognosis. While researches in Statistical Process Control (SPC) tools and Condition-Based Maintenance (CBM) are tremendous, their applications considering high-dimensional data sets has received less attention due to the complexity and challenging nature of such data and its analysis. This thesis adds to this field by designing a Deep Learning (DL) based survival analysis model towards CBM in the prognostic context and a DL and SPC based hybrid model for process diagnosis, both using high dimensional data. In the first part, we a design support system for maintenance decision making by considering degradation signals obtained from CM data. The decision support system in place can predict system’s failure probability in a smart way. In the second part, a Fast Region-based Convolutional Network (Fast R-CNN) model is applied to monitor the video input data. Then, specific statistical features are derived from the resulting bounding boxes and plotted on the multivariate Exponentially Weighted Moving Average (EWMA) control chart to monitor the process

Safety and Reliability - Safe Societies in a Changing World

The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen