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

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

Fostering work ability among menopausal women. Does any work-related psychosocial factor help?

Introduction: Due to the aging workforce, it will become even more common for organizations to count, among their employees, women who are dealing with menopause. To date, no knowledge is available regarding the work ability among menopausal women. With this view, the aim of the present study was to identify work-related psychosocial factors associated with work ability in a sample of menopausal working women. Methods: A self-report questionnaire was administered to 1069 menopausal women employed as administrative officers in the Italian public sector. The study design was cross-sectional. Results: Work ability was found to be negatively associated with family–work conflict (β = − 0.21, p = 0.0001) and positively associated with health-oriented organizational climate (β = 0.12, p = 0.0001), job autonomy (β = 0.08, p= 0.006), and skill discretion (β = 0.08, p= 0.048). Conversely, work ability did not show significant associations with job demands, flexible working hours, and social support. Discussion: From a practical point of view, our study identifies various areas of intervention that could foster job sustainability during menopause. In particular, our findings suggest that, to improve women’s job sustainability across their entire work-life span, it may be crucial to develop organizational policies, training, and activities specifically dedicated to sustaining menopausal women's well-being

Implementation and validation of a new method to model voluntary departures from emergency departments

In the literature, several organizational solutions have been proposed for determining the probability of voluntary patient discharge from the emergency department. Here, the issue of self-discharge is analyzed by Markov theory-based modeling, an innovative approach diffusely applied in the healthcare field in recent years. The aim of this work is to propose a new method for calculating the rate of voluntary discharge by defining a generic model to describe the process of first aid using a “behavioral” Markov chain model, a new approach that takes into account the satisfaction of the patient. The proposed model is then implemented in MATLAB and validated with a real case study from the hospital “A. Cardarelli” of Naples. It is found that most of the risk of self-discharge occurs during the wait time before the patient is seen and during the wait time for the final report; usually, once the analysis is requested, the patient, although not very satisfied, is willing to wait longer for the results. The model allows the description of the first aid process from the perspective of the patient. The presented model is generic and can be adapted to each hospital facility by changing only the transition probabilities between states