Rotating machines play a vital role in many industrial applications, ranging from power generation to manufacturing. The early detection of mechanical faults in industrial rotating machines is crucial for enhancing the reliability and safety of industrial systems. This research focuses on rotating machines that operate under varying working conditions, which exhibit specific characteristics that make the development of practical data-driven fault diagnostics methods challenging. The paper proposes a methodology that aims to develop robust techniques that can operate effectively in real industrial environments. To simulate rotating machinery operating under varying working conditions, an experimental protocol is conducted, and the resulting vibration signals are analyzed. Finally, the study describes an approach to train data-driven methods based on realistic data availability scenarios.

In the prognostics and health management (PHM) of industrial systems, prediction of remaining useful life (RUL) is a crucial task. RUL prediction is based on data collected from the industrial system, and involves learning underlying health indicator trends. As industrial systems are complex and can be monitored by different sensors, time alignment of multiple temporal data streams and extraction of their underlying characteristics are essential to perform an accurate prognostics. Hence, this paper aims to develop an efficient method to address the above issue. The proposed method is based on the attention and convolution mechanisms of deep neural networks. Its performance is highlighted when compared to other state of the art models such as RNN and LSTM using the C-MAPSS datasets. Numerous experiments demonstrate that our model provides better results in some situations, as well as an ability to capture both local short term contexts and long term associations.

Democratization of sensing devices in industrial systems has made it possible to collect a large amount of data of different types, which has led to the necessity of handling complex analyses for knowledge extraction . The field of water resources is of those areas which has drawn the attention of decision-makers seeking to preserve human health and safety. Recent advances in Artificial Intelligence, particularly in the domain of Machine Learning, have opened the potential to leverage massive data to better address the issue related to the relationship between water quality and human activities. However, high rate of missing data and heterogeneity of the measurements are scientific issues that cannot be solved by standard methods, especially when no prior knowledge on the label of each observation is provided. In this article, Prognostics and Health Management was implemented to detect and diagnose anomalies in water quality datasets, taking into account the uncertainties induced by the above-mentioned issues. Fuzzy c-means was used to identify the different water quality classes, while Random Forest was applied to determine the most influencing parameters, with respect to potential contamination of water resources in the southwest of France. The results suggest that multiple imputation methods can handle the missingness issue, while the use of decision rules based on well-known water quality standards can solve the problem regarding the lack of labelled observations. In addition, two potential sources of contamination (atrazine and nitrate) were identified and then validated by hydrogeology experts, prior to further online deployment of the proposed model.

Due to population growth and rapid urbanization, the demand for mobility has increased over the past decade. In this context, the paradigm has shifted from mobility aimed only at increasing the transport capacity of goods and people to service-oriented mobility, aimed at ensuring sustainability and satisfying the new needs of citizens. This situation leads to the emergence of new problems such as managing the transformation of urban mobility without reducing its sustainability and anticipating future developments. From this point of view, the design of a sustainable mobility system requires an approach that integrates the entire life cycle of the system, allows interoperability between autonomous systems and makes it possible to manage the complexity it implies. In this paper, a method combining engineering systems and knowledge engineering approaches to manage the complexity of assessing the sustainability of an urban mobility system is proposed. The result has been the development of an ontology that better characterizes the needs of all actors involved in urban mobility and represents the complex interactions between their subsystems.