An Online Anomaly Detection Approach for Fault Detection on Fire Alarm Systems.

The early detection of fire is of utmost importance since it is related to devastating threats regarding human lives and economic losses. Unfortunately, fire alarm sensory systems are known to be prone to failures and frequent false alarms, putting people and buildings at risk. In this sense, it is essential to guarantee smoke detectors' correct functioning. Traditionally, these systems have been subject to periodic maintenance plans, which do not consider the state of the fire alarm sensors and are, therefore, sometimes carried out not when necessary but according to a predefined conservative schedule. Intending to contribute to designing a predictive maintenance plan, we propose an online data-driven anomaly detection of smoke sensors that model the behaviour of these systems over time and detect abnormal patterns that can indicate a potential failure. Our approach was applied to data collected from independent fire alarm sensory systems installed with four customers, from which about three years of data are available. For one of the customers, the obtained results were promising, with a precision score of 1 with no false positives for 3 out of 4 possible faults. Analysis of the remaining customers' results highlighted possible reasons and potential improvements to address this problem better. These findings can provide valuable insights for future research in this area.

The MetroPT dataset for predictive maintenance.

The paper describes the MetroPT data set, an outcome of a Predictive Maintenance project with an urban metro public transportation service in Porto, Portugal. The data was collected in 2022 to develop machine learning methods for online anomaly detection and failure prediction. Several analog sensor signals (pressure, temperature, current consumption), digital signals (control signals, discrete signals), and GPS information (latitude, longitude, and speed) provide a framework that can be easily used and help the development of new machine learning methods. This dataset contains some interesting characteristics and can be a good benchmark for predictive maintenance models.

A Survey on Data-Driven Predictive Maintenance for the Railway Industry.

In the last few years, many works have addressed Predictive Maintenance (PdM) by the use of Machine Learning (ML) and Deep Learning (DL) solutions, especially the latter. The monitoring and logging of industrial equipment events, like temporal behavior and fault events-anomaly detection in time-series-can be obtained from records generated by sensors installed in different parts of an industrial plant. However, such progress is incipient because we still have many challenges, and the performance of applications depends on the appropriate choice of the method. This article presents a survey of existing ML and DL techniques for handling PdM in the railway industry. This survey discusses the main approaches for this specific application within a taxonomy defined by the type of task, employed methods, metrics of evaluation, the specific equipment or process, and datasets. Lastly, we conclude and outline some suggestions for future research.

Latex membranes with methylene blue dye for antimicrobial photodynamic therapy.

The search for new materials that can be applied in the treatment of injured human tissues has led to the development of new dressings. Membranes have potential as dressing materials because they can be fitted to and interact with the tissue surface. In this study, we analyze the morphological properties and wettability of latex membranes, along with the incorporation of the photosensitizer methylene blue, in the context of the utility of the membranes in curative applications involving photodynamic therapy (PDT). It was observed that deposition of the photosensitizer into latex membranes increased both the surface roughness and wettability. Antifungal testing indicated that antimicrobial PDT assisted by the latex membranes incorporating methylene blue effectively inactivated Candida albicans.