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

Big Data and the Internet of Things

Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things - IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to "improve" their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski (eds.) Big Data Analysis: New algorithms for a new society, Springer Series on Studies in Big Data, to appea

Ontologias para Manutenção Preditiva com Dados sensíveis ao tempo

As empresas de fabrico industrial devem assegurar um processo produtivo contínuo para serem competitivas e fornecer os produtos fabricados no prazo e com a qualidade exigida pelos clientes. A quebra da cadeia de fabrico pode ter desfechos graves, resultando numa redução da produção e na interrupção da cadeia de abastecimento. Estes processos são compostos por cadeias de máquinas que executam tarefas em etapas. Cada máquina tem uma tarefa específica a executar, e o resultado de cada etapa é fornecido à próxima etapa. Uma falha imprevista numa das máquinas tende a interromper toda a cadeia produtiva. A manutenção preventiva agendada tem como objetivo evitar a ocorrência de falhas, tendo como base o tempo médio antes da falha (MTBF), que representa a expectativa média de vida de componentes individuais com base em dados históricos. As tarefas de manutenção podem implicar um período de paralisação e a interrupção da produção. Esta manutenção é executada rotineiramente e a substituição de componentes não considera a necessidade premente da sua substituição, sendo os mesmos substituídos com base no ciclo do agendamento. É aqui que a manutenção preditiva é aplicável. Efetuando a recolha de dados de sensores dos equipamentos, é possível detetar irregularidades nos dados recolhidos, através da aplicação de processos de raciocínio e inferência, conduzindo à atempada previsão e deteção de falhas. Levando este cenário à otimização do tempo de manutenção, evitando falhas inesperadas, à redução de custos e ao aumento da produtividade em comparação com a manutenção preventiva. Os dados fornecidos pelos sensores são sensíveis ao tempo, variações e flutuações ocorrem ao longo do tempo e devem ser analisados em relação ao período em que ocorrem. Esta dissertação tem como objetivo o desenvolvimento de uma ontologia para a manutenção preditiva que descreva a sua abrangência e o campo da sua aplicação. A aplicabilidade da ontologia será demonstrada com uma ferramenta, igualmente desenvolvida, que transforma dados sensíveis ao tempo recolhidos em tempo real a partir de sensores de máquinas industriais, fornecidos por WebServices, em indivíduos dessa mesma ontologia, considerando a representação do fator temporal dos dados.Manufacturing companies must ensure a continuous production process to be competitive and supply the manufactured goods in time and with the desired quality the customers expect. Any disruption in the manufacturing chain may have disastrous consequences, representing a shortage of production and the interruption of the supply chain. The manufacturing processes are composed of a chain of industrial machines operating in stages. Each machine has a specific task to complete, and the result of each stage is forwarded to the next stage. An unpredicted malfunction of one of the machines tends to interrupt the whole production chain. Scheduled Preventive maintenance intends to avoid causes leading to faults, but relies on parameters such as Mean Time Before Failure (MTBF), which represents the average expected life span of individual components based on statistical data. A maintenance task may lead to a period of downtime and consequently to a production halt. Being the maintenance scheduled and executed routinely, the replacement of components, does not consider the effective need of its replacement, they are replaced based on the scheduling cycle. This is where predictive maintenance is applicable. By collecting sensor data of industrial equipment, anomalies can be determined through reasoning and inference processes applied to the data, leading to an early fault and time to failure prediction. This scenario leads to maintenance timing optimization, avoidance of unexpected failures, cost savings and improved productivity when compared to preventive maintenance. Data supplied by sensors is timesensitive, as variations and fluctuations occur over periods of time and must be analysed concerning the period they occur. This dissertation aims to develop an ontology for predictive maintenance that describes the scope and field of application. The applicability of the ontology will be demonstrated with a tool, also to be developed, that transforms time-sensitive data collected in real time from sensors of industrial machines, provided by a WebServices, into individuals of the same ontology, considering the representation of the temporal factor of the data

Continuous maintenance and the future – Foundations and technological challenges

High value and long life products require continuous maintenance throughout their life cycle to achieve required performance with optimum through-life cost. This paper presents foundations and technologies required to offer the maintenance service. Component and system level degradation science, assessment and modelling along with life cycle ‘big data’ analytics are the two most important knowledge and skill base required for the continuous maintenance. Advanced computing and visualisation technologies will improve efficiency of the maintenance and reduce through-life cost of the product. Future of continuous maintenance within the Industry 4.0 context also identifies the role of IoT, standards and cyber security