Predicting Failure Probability in Industry 4.0 Production Systems: A Workload-Based Prognostic Model for Maintenance Planning

Abstract

Maintenance of equipment is a crucial issue in almost all industrial sectors as it impacts the quality, safety, and productivity of any manufacturing system. Additionally, frequent production rescheduling due to unplanned and unintended interruptions can be very time consuming, especially in the case of centrally controlled systems. Therefore, the ability to estimate the likelihood that a monitored machine will successfully complete a predefined workload, taking into account both historical data from the machine’s sensors and the impending workload, may be essential in supporting a new approach to scheduling activities in an Industry 4.0 production system. This study proposes a novel approach for integrating machine workload information into a well-established PHM algorithm for Industry 4.0, with the aim of improving maintenance strategies in the manufacturing process. The proposed approach utilises a logistic regression model to assess the health condition of equipment and a neural network computational model to estimate its failure probability according to the scheduled workloads. Results from a prototypal case study showed that this approach leads to an improvement in the prediction of the likelihood of completing a scheduled job, resulting in improved autonomy of CPSs in accepting or declining scheduled jobs based on their forecasted health state, and a reduction in maintenance costs while maximising the utilisation of production resources. In conclusion, this study is beneficial for the present research community as it extends the traditional condition-based maintenance diagnostic approach by introducing prognostic capabilities at the plant shop floor, fully leveraging the key enabling technologies of Industry 4.0.

An Unsupervised Anomaly Detection Based on Self-Organizing Map for the Oil and Gas Sector

“…4. The black dots indicate the samples in the node [6,10]. This result is confirmed further by the fact that when the shut-off valve is triggered, the system needs some time to migrate to a new operating point.…”

“…In this case, dark-coloured boxes indicate those nodes that are recalled the most during the training phase and vice versa for the light-coloured boxes. The most responsive nodes have coordinates [5,10] and [6,10]. As shown in Figure 10, all readings of the system's steady state are placed in these two locations.…”

“…Petrochemical industries, for example, are keen to embrace digital technologies with all the predictive benefits that come with them [4]. For instance, by combining plant sensors and artificial intelligence algorithms, it is possible to build a model that can discriminate the degradation profiles and estimate the remaining useful life of each system component [5,6].…”

Smart Preventive Maintenance of Hybrid Networks and IoT Systems Using Software Sensing and Future State Prediction

“…Therefore, many researchers are focused on finding the most appropriate solutions to improve efficiency of this aspect. Different strategies are tested, and they prove their efficacity in increasing reliability [23], such as using a logistic regression model to assess the health condition of equipment and a neural network model to estimate its failure probability, considering the scheduled workloads. Besides the industrial process, employment of intelligent agents to verify on a continuous basis the load of different components in a communication network has also been implemented.…”

Developing an Integrated IoT Cloud Based Predictive Conservation Model for Asset Management in Industry 4.0

“…Ullah et al [18] proposed multi-level strategy which shows how federated learning, IoT, and crowdsourcing could work together to produce a self-sustaining federated learning ecosystem well-suited to Industry 4.0. All of these help pave the way for future intelligent applications in the Industry 4.0 sphere.…”