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Data-driven prognostics of systems exploit sensor measurements to predict the degradation evolution and anticipate failures, corresponding to the estimation of the remaining useful life (RUL). This task uses feature engineering to build prognostic indicators (HI) and machine learning (ML) to estimate the RUL. However, high variability in data coming from similar systems operating under different conditions negatively affects the RUL performance. Hence, this paper presents a new methodology that combines feature and ML engineering methods to provide an explainable RUL prediction. The key contributions lie in constructing efficient prognostic indicators that isolate distinct profile trajectories, enabling adaptive RUL extraction for each system. An ensemble of heterogeneous ML predictors is also trained using these indicators and RUL trajectories, effectively addressing variability issues and enhancing RUL performance. The proposed methodology is rigorously validated using NASA-provided turbofan engine data (C-MAPSS), demonstrating its performance, compared to state-of-the-art results, with improved score and accuracy of prediction. • Explainability of RUL calculation considering the variability of degradation trajectories. • Enhancing the adaptability of predictions to different operating conditions. • Handling epistemic uncertainties in RUL estimation. • High accuracy and precision of the estimated remaining useful life. [ABSTRACT FROM AUTHOR]

• Proposition of a practical and effective methodology for condition monitoring. • Construction of a new health indicator based on current signals. • Utilization of current signals to detect bearing, gear and combined faults. • Fault classification based on an improved ANFIS model. • Application of the proposed methodology on gearbox fault detection and diagnostics. Gear reducer motors play an important role in industry due to their robustness and simplicity of construction. However, the appearance of faults in these systems can affect the quality of the product and lead to significant financial losses. Therefore, it is necessary to perform Prognostics and Health Management (PHM) for these systems. This paper aims to develop a practical and effective method allowing an early fault detection and diagnostic for critical components of the gear reducer, in particular gear and bearing defects. This method is based on a new indicator extracted from electrical signals. It allows characterizing different states of the gear reducer, such as healthy state, bearing faults, gear faults, and combined faults. The diagnostic of these states is done by the Adaptive Neuro-Fuzzy Inference System (ANFIS). The efficiency and the robustness of the proposed method are highlighted through numerous experimental tests with different levels of loads and speeds. [ABSTRACT FROM AUTHOR]

• Proposition of a practical and effective method for condition monitoring. • Construction of a new health indicator based on different data-types. • Utilization of electrical signals to detect most electrical machine defects. • Fault classification based on an improved ANFIS model. • Application of the proposed methodology on various systems. Smart manufacturing is one of the key parts of the fourth industry revolution (Industry 4.0). It offers promising perspectives for high reliability, availability, maintainability and safety production process, but also makes the systems more complex and challenging for health assessment. To deal with these challenges, one needs to develop a robust approach to monitor and assess the system health state. In this paper, a practical and effective method that can be applied for fault detection and diagnostics of a given system is developed. The proposed method relies on a pattern recognition technique based on the construction of a new health indicator. This health indicator, which can be applied to different types of sensor measurements, is fed to an Adaptive Neuro-Fuzzy Inference System (ANFIS) to detect the health states of the system and diagnose the causes. Furthermore, the performance and the robustness of the proposed method are highlighted by considering various case studies under numerous operating conditions. [ABSTRACT FROM AUTHOR]