The development of CMMS incorporating condition monitoring tools in the advances of Industry 4

Computerized maintenance management software (CMMS) considered effective supporting tools to enhance the organisation and scheduling practices of maintenance tasks on manufacturing assets. Condition monitoring applications in the advances of Industry 4.0 applications enhances machines condition insight by utilising different sensing nodes to improve the optimisation of the scheduled maintenance tasks and support predictive maintenance applications. To overcome the disconnection between condition monitoring technology and CMMS software, the research presents a new generation of CMMS by integrating condition monitoring technologies with maintenance management functionalities under a single cloud-based platform. As an example, energy data from five-axis machine tools are included to show it is predictable and stable to be reliable for failures prediction applications

Development of maintenance framework for modern manufacturing systems

Modern manufacturing organizations are designing, building and operating large, complex and often ‘one of a kind’ assets, which incorporate the integration of various systems under modern control systems. Due to such complexity, machines failures became more difficult to interpret and rectify and the existing maintenance strategies became obsolete without development and enhancement. As a result, the need for more advanced strategies to ensure effective maintenance applications that ensures high operation efficiency arise. The current research aims to investigate the existing maintenance strategies, the levels of machines complexity and automation within manufacturing companies from different sectors and sizes including, oil and gas, food and beverages, automotive, aerospace, and Original Equipment Manufacturer. Results analysis supports in the development of a modern maintenance framework that overcome the highlighted results and suits modern manufacturing assets using systematic approaches and utilisation of pillars from Total productive maintenance (TPM, Reliability Centred Maintenance (RCM) and Industry 4.0

Comparative Analysis of Machine Learning Models for Predictive Maintenance of Ball Bearing Systems

In the era of Industry 4.0 and beyond, ball bearings remain an important part of industrial systems. The failure of ball bearings can lead to plant downtime, inefficient operations, and significant maintenance expenses. Although conventional preventive maintenance mechanisms like time-based maintenance, routine inspections, and manual data analysis provide a certain level of fault prevention, they are often reactive, time-consuming, and imprecise. On the other hand, machine learning algorithms can detect anomalies early, process vast amounts of data, continuously improve in almost real time, and, in turn, significantly enhance the efficiency of modern industrial systems. In this work, we compare different machine learning and deep learning techniques to optimise the predictive maintenance of ball bearing systems, which, in turn, will reduce the downtime and improve the efficiency of current and future industrial systems. For this purpose, we evaluate and compare classification algorithms like Logistic Regression and Support Vector Machine, as well as ensemble algorithms like Random Forest and Extreme Gradient Boost. We also explore and evaluate long short-term memory, which is a type of recurrent neural network. We assess and compare these models in terms of their accuracy, precision, recall, F1 scores, and computation requirement. Our comparison results indicate that Extreme Gradient Boost gives the best trade-off in terms of overall performance and computation time. For a dataset of 2155 vibration signals, Extreme Gradient Boost gives an accuracy of 96.61% while requiring a training time of only 0.76 s. Moreover, among the techniques that give an accuracy greater than 80%, Extreme Gradient Boost also gives the best accuracy-to-computation-time ratio