Current trends in the Industrial Internet of Things (IIoT) have increased the sensorization of systems, thus increasing data availability to apply data-driven fault detection and diagnosis techniques to monitor these systems. In this work, we show the capabilities of an information-driven method for detecting and quantifying faults in a subsystem common among a broad range of industries: the conical tank. Our main experiment consists of using a simple black-box model (multi-layer perceptron -- MLP) to capture the dynamics of a PID-controlled conical tank built in Simulink and then induce pump failures of different severities; the derived data-driven indicators that we developed increase with the severity of the fault validating its usefulness in this controlled setting. A complementary experiment is carried out to enrich our analysis; this consists of simulating an open-loop discrete-time version of the conical tank to explore a range of fault severity and analyze the distribution of the indicators across this range. All our results show the applicability of the data-driven fault monitoring method in conical tanks subjected to either open- or closed-loop operation.

Lithium-ion battery (LIB) lifetime prognosis remains a decisive enabler for safe, economical, and sustainable electrification across mobility and stationary energy sectors. Despite the maturity of this storage technology, the impact of capacity reduction due to battery aging remains a subject of interest to this day. This study presents a degradation model that computes the incremental loss of usable capacity for every equivalent cycle experienced by the battery. The model depends on the State of Charge values and the mean ambient temperature at which the cycle occurred. The methodology generalizes the results of a previous study where an initial degradation model was calibrated on long‑term cycling data at which multiple commercial cells were driven to their end of life under varied state of charge ranges in addition to a chemical analysis of cell's degradation due to different operating temperatures. Unlike traditional calendar-plus-cycling additives or chemistry-specific semi-empirical methods, the proposed model is formulated—and further validated—under the assumption that cells exposed to comparable operating conditions degrade at comparable rates, independent of their electrode chemistry. Consequently, only the data of a single degradation campaign at a given operational mode is needed, which is typically found in the cell's datasheet, eliminating the need for time-consuming aging tests. Validation is performed using a public experimental dataset provided by the University of Stanford dataset. The results show the model's ability to predict the end-of-life within one percent of the reported battery State of Health (SOH), even for an electric vehicle usage profile. By embedding the framework in battery management systems, fleet operators can estimate remaining useful life (RUL) based on historical operational data, optimize battery usage based on less degrading SoC values and cooling systems, schedule preventive maintenance before catastrophic capacity loss, and compare alternative uses without the need for degradation studies. These contributions align with conference themes on prognostics, health management, and data-driven energy system optimization, offering a practical pathway towards longer-lived and better-valued electrified assets.

Stator short-circuit faults (SSCFs) account for a significant portion of induction motor failures, yet their early detection remains a challenge in industrial environments where labeled fault data is scarce and installing additional sensors is often impractical. This paper proposes a novel, data-driven fault detection and diagnosis framework grounded in the Residual Information Value (RIV) principle to overcome reliability limitations of traditional spectral and residual energy methods. By redefining fault detection as a statistical test of independence between control inputs (voltages) and current residuals, the proposed method identifies incipient faults as model drifts without relying on prior knowledge of fault distributions. A key contribution of this work is the seamless integration of the diagnostic scheme into standard Variable Speed Drives (VSDs): the healthy nominal model (a Multilayer Perceptron) is trained exclusively using data from the drive’s existing self-commissioning routine, eliminating the need for manual data collection or complex physical parameter identification. Experimental validation on an industrial test bench demonstrates that the framework achieves superior diagnostic performance compared to traditional baselines, providing higher statistical separability and a reduced false alarm rate. The system can detect 1% incipient faults in approximately 61 ms while accurately identifying the faulty phase. The results confirm that the proposed RIV-based strategy offers a robust, non-intrusive, and industry-ready solution for predictive maintenance that effectively balances high-speed detection with enhanced statistical reliability.