There are three types of maintenance management policy Run-tofailure (R2F), Preventive Maintenance (PvM) and Predictive Maintenance (PdM). In both R2F and PdM we have the data related to the maintenance cycle. In case of Preventive Maintenance (PvM) complete information about maintenance cycle is not available. Among these three maintenance policies, predictive Maintenance (PdM) is becoming a very important strategy as it can help us to minimize the repair time and the associated cost with it. In this paper we have proposed PdM, which allows the dynamic decision rules for the maintenance management. PdM is achieved by training the machine learning model with the datasets. It also helps in planning of maintenance schedules. We specially focused on two models that are Binary Classification and Recurrent Neural Network. In Binary Classification we classify whether our data belongs to the failure class or the non failure class. In Binary Classification the number of cycles is entered and classification model predicts whether it belongs to the failure/non failure class.

As a major global health challenge, the progressive cognitive decline and memory loss associated with Alzheimer's disease (AD) significantly impact individuals' quality of life. Due to the lack of a definitive cure, early and precise diagnosis remains crucial for implementing effective intervention and management strategies. In this study, we introduce a pioneering approach utilizing Transformer based ResLadderNet for AD classification, achieving an exceptional accuracy of 98%. The classification of AD is crucial for several reasons. Firstly, accurate classification aids in early detection, allowing healthcare professionals to implement interventions aimed at mitigating disease progression and enhancing patient outcomes. Secondly, it allows for stratification of patients based on disease severity and subtype, which is essential for personalized treatment planning and clinical trials. Moreover, effective classification supports research efforts aimed at understanding disease mechanisms and identifying biomarkers that could further enhance diagnostic accuracy and therapeutic development. The ResLadderNet architecture leverages the strengths of residual networks and ladder networks, optimizing feature extraction and hierarchical representation learning with transformer to feature propagation. This approach not only enhances the interpretability of the model's decisions but also improves its robustness against variations in imaging data quality and disease progression stages. Our methodology employs a meticulously curated dataset of 10,240 samples across four distinct classes, sourced from Harvard, to train and validate the model. This model achieves outstanding performance with an accuracy of 98.93%. Notably, this study demonstrates remarkable performance, achieving precision at 0.98, recall at 0.97, and an F1 score of 0.98, underscoring its efficacy in detecting early indicators of Alzheimer's disease. This breakthrough has significant implications for early intervention and treatment, potentially improving the lives of millions worldwide.