Assessing the Technical Specifications of Predictive Maintenance: A Case Study of Centrifugal Compressor

Dependability analyses in the design phase are common in IEC 60300 standards to assess the reliability, risk, maintainability, and maintenance supportability of specific physical assets. Reliability and risk assessment uses well-known methods such as failure modes, effects, and criticality analysis (FMECA), fault tree analysis (FTA), and event tree analysis (ETA)to identify critical components and failure modes based on failure rate, severity, and detectability. Monitoring technology has evolved over time, and a new method of failure mode and symptom analysis (FMSA) was introduced in ISO 13379-1 to identify the critical symptoms and descriptors of failure mechanisms. FMSA is used to estimate monitoring priority, and this helps to determine the critical monitoring specifications. However, FMSA cannot determine the effectiveness of technical specifications that are essential for predictive maintenance, such as detection techniques (capability and coverage), diagnosis (fault type, location, and severity), or prognosis (precision and predictive horizon). The paper proposes a novel predictive maintenance (PdM) assessment matrix to overcome these problems, which is tested using a case study of a centrifugal compressor and validated using empirical data provided by the case study company. The paper also demonstrates the possible enhancements introduced by Industry 4.0 technologies.publishedVersio

Designing Predictive Maintenance for Agricultural Machines

The Digital Transformation alters business models in all fields of application, but not all industries transform at the same speed. While recent innovations in smart products, big data, and machine learning have profoundly transformed business models in the high-tech sector, less digitalized industries—like agriculture—have only begun to capitalize on these technologies. Inspired by predictive maintenance strategies for industrial equipment, the purpose of this paper is to design, implement, and evaluate a predictive maintenance method for agricultural machines that predicts future defects of a machine’s components, based on a data-driven analysis of service records. An evaluation with 3,407 real-world service records proves that the method predicts damaged parts with a mean accuracy of 86.34%. The artifact is an exaptation of previous design knowledge from high-tech industries to agriculture—a sector in which machines move through rough territory and adverse weather conditions, are utilized extensively for short periods, and do not provide sensor data to service providers. Deployed on a platform, the prediction method enables co-creating a predictive maintenance service that helps farmers to avoid resources shortages during harvest seasons, while service providers can plan and conduct maintenance service preemptively and with increased efficiency

Intelligent maintenance of complex systems: a fractional order approach

Compete in the global market requires high quality of products with short time of manufacture, so it needs to minimize the time that the machinery is stopped, as well as a rapid quality control of manufactured products. These process are achieved by maintenance strategies that are strongly based on the subjective knowledge of an expert. In this work we use the proficiency of the fractional order calculus to approximate complex behavior with a few parameter, providing a new tool for quickly health evaluation.N/

About intelligent maintenance and diagnosis techniques for mechatronic systems : case study using fractional order calculus

Orientadores: João Maurício Rosário, José António Tenreiro MachadoTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia MecânicaResumo: A competitividade no mercado global exige cada vez mais a fabricação de produtos de alta qualidade em curto tempo de fabricação, evitando tempos de parada para manutenção e reparo de máquinas e equipamentos, exigindo assim um eficiente controle de qualidade do processo e dos produtos para evitar a ocorrência de falhas de fabricação e utilização. A integração de novas tecnologias em produtos industriais (ex. tecnologias mecatrônicas) exige a utilização de técnicas avançadas para o diagnóstico de falhas, a partir de análise dos sinais obtidos a partir do sensoriamento dos equipamentos, minimizando assim os custos de utilização de mão de obra especializada para controle de qualidade do produto. Neste trabalho é apresentado inicialmente, um estudo sobre o estado da arte em técnicas de manutenção industrial, com ênfase nas estratégias utilizadas para manutenção corretiva, periódica e baseada no comportamento com ênfase no estudo das técnicas de processamento do sinal e identificação de sistemas, frequentemente utilizadas no diagnóstico de sistemas mecatrônicos, que exigem uma grande quantidade de informações, e forte dependência da análise criteriosa de um técnico especializado. Assim, neste trabalho são utilizados sistemas de ordem fracionária, que permite a aproximação do comportamento real do sistema por meio de modelos com menos coeficientes que o sistema real, simplificando a análise do sistema em estudo. Um estudo experimental de caso para validação do trabalho é realizado a partir de uma bancada experimental de um sistema de transmissão por engrenagens que permitiu introduzir falhas particulares no sistema e sua identificaçãoAbstract: The global market competitiveness requires to make high quality products in a short time of manufacturing, avoiding stop-times due to maintenance and repairing of machines and devices, therefore, demanding an efficient quality control of the manufacturing process, in order to shun failures in fabrication and utilization. The integration of new technologies into industrial products (e.g. mechatronics technologies) requires the use of advanced techniques to a precise failure diagnosis. They are typically based on signal analyses, which are obtained from the machines' instrumentation, and consequently, reduce the manpower costs associated to quality control of particular products. In this work it is introduced a literature review of industrial maintenance techniques, focusing in the strategies used into corrective, periodic and condition based maintenance, specially using signal processing and system identification. Those paradigms are frequently applied into the mechatronics systems diagnosis, but requires a huge amount of information and it is strongly dependent on the specialist criterion. In this sense, we introduced a fractional order system approach, which results in a better approximation of the actual system through an few parameters architecture, hence simplifying the analysis of the actual system. A real experimental setup was used to validate the strategies studied in this work. It consist in a gear transmission that lets to introduce particular failures for a posterior identificationDoutoradoMecanica dos Sólidos e Projeto MecanicoDoutor em Engenharia Mecânic

Digital transformation in the manufacturing industry : business models and smart service systems

The digital transformation enables innovative business models and smart services, i.e. individual services that are based on data analyses in real-time as well as information and communications technology. Smart services are not only a theoretical construct but are also highly relevant in practice. Nine research questions are answered, all related to aspects of smart services and corresponding business models. The dissertation proceeds from a general overview, over the topic of installed base management as precondition for many smart services in the manufacturing industry, towards exemplary applications in form of predictive maintenance activities. A comprehensive overview is provided about smart service research and research gaps are presented that are not yet closed. It is shown how a business model can be developed in practice. A closer look is taken on installed base management. Installed base data combined with condition monitoring data leads to digital twins, i.e. dynamic models of machines including all components, their current conditions, applications and interaction with the environment. Design principles for an information architecture for installed base management and its application within a use case in the manufacturing industry indicate how digital twins can be structured. In this context, predictive maintenance services are taken for the purpose of concretization. It is looked at state oriented maintenance planning and optimized spare parts inventory as exemplary approaches for smart services that contribute to high machine availability. Taxonomy of predictive maintenance business models shows their diversity. It is viewed on the named topics both from theoretical and practical viewpoints, focusing on the manufacturing industry. Established research methods are used to ensure academic rigor. Practical problems are considered to guarantee practical relevance. A research project as background and the resulting collaboration with different experts from several companies also contribute to that. The dissertation provides a comprehensive overview of smart service topics and innovative business models for the manufacturing industry, enabled by the digital transformation. It contributes to a better understanding of smart services in theory and practice and emphasizes the importance of innovative business models in the manufacturing industry