Hybrid systems combine both continuous and discrete behavior. These systems serve as models in many fields, including control systems, robotics, and industrial processes. However, due to their complexity, finding an accurate model is a challenge. This paper presents a holistic approach to learning models of hybrid systems using symbolic regression. Our method leverages symbolic regression to automatically discover accurate and interpretable mathematical models in the form of hybrid systems from observed data. An advantage of our algorithm is that it detects transitions between different behavioral modes of a system based on the inherent dynamics. From learned expressions for the dynamical behavior of a system, we form a hybrid system by combining the learned expressions with a decision tree determining the current behavioral mode from data. This hybrid decision tree serves regression, prediction, and further related tasks. Our results demonstrate that symbolic regression can effectively identify the underlying dynamics of a real hybrid system and predict output signals on new input data with high accuracy. • Hybrid systems are powerful models combining continuous and discrete dynamics. • A data-driven identification automates model generation. • Often, dynamic modes are identified from signal similarities. • Different initial conditions lead to dissimilar signals even for same dynamics. • Dynamics-based identification is a more effective approach.

Hybrid systems, which combine both continuous and discrete behavior, are used in many fields, including robotics, biological systems, and control systems. However, due to their complexity, finding an accurate model is a challenge. This paper discusses the usage of symbolic regression to learn hybrid systems from data and specifically analyses learning parameters for a recent algorithm. Symbolic regression is a powerful tool that can automatically discover accurate and interpretable mathematical models in the form of symbolic expressions. Models generated by symbolic regression are a valuable tool for system identification and diagnosis, e.g., to predict future system behavior or detect anomalies. A major opportunity of our approach is the ability to detect transitions between different continuous behaviors of a system directly based on the dynamics. From a diagnosis perspective, this can advantageously be used to detect the system entering fault modes and identify their models. This paper presents a parameter study for a symbolic regression based identification algorithm.

Symbolic regression has shown potential in the identification of physical systems. Hybrid systems, which combine both continuous and discrete behavior, are a relevant extension of purely physical systems, used in many fields, including robotics, biological systems, and control systems. However, due to their complexity, finding an accurate model is a challenge. This paper presents a novel approach to learning models of hybrid systems using symbolic regression. Our method leverages the power of genetic programming to automatically discover accurate and interpretable mathematical models in the form of hybrid systems from observed data. Symbolic regression detects transitions between different continuous behavior of a system directly based on the dynamics, instead of pure distances of observed trajectories. Furthermore, models generated by symbolic regression can be used to predict future system behavior, detect anomalies, and identify the underlying dynamics of the system while providing a human-readable representation. Our results demonstrate that symbolic regression can effectively identify the underlying dynamics of a real system represented in a hybrid model, providing a valuable tool for system identification and diagnosis.

This research proposes a method for detecting subtle faults named snail trails for their visual similarity with the trail of a snail in photovoltaic modules. Snail trails do not significantly reduce panel performance but they are the main cause of serious panel deterioration such as microcracks and delamination and can go so far as to set the panel on fire. To detect these faults, this research uses an ensemble learning framework, named ensemble learning for diagnosis, which combines several complementary learning algorithms, namely Support Vector Machines, K-Nearest Neighbors, and Decision Trees. A set of features is obtained by extracting the time–frequency characteristics and statistics from the photovoltaic current signal of the photovoltaic panel. This is followed by a feature selection and dimensionality reduction step that delivers the input to the learning algorithms. The approach presented in this study is experimentally validated, independently for the 4 seasons of the year, with data from a real photovoltaic string of 16 panels. The results demonstrate that the proposed approach can efficiently classify healthy panels and panels with snail trails efficiently. Interestingly, the method only requires the electrical current signal, measured on panels with data acquisition systems that are standard in the photovoltaic industry. The genericity of the approach makes it a good candidate for detecting other photovoltaic faults and for solving diagnosis problems in other domains.

Checking the diagnosability of a timed discrete-event system usually consists in determining whether a single fault event can always be identified with certainty after a finite amount of time. The aim of this article is to extend this type of analysis to more complex behaviors, called event patterns, and to propose an effective method to check diagnosability with the use of model-checking techniques. To do so, we propose to convert the pattern diagnosability problem into checking a linear-time property over a specific time Petri net.Note to Practitioners—This article is motivated by the problem of improving the monitoring and the supervision of systems, such as automated and robotized manufacturing systems. Based on a model of the system, this article proposes a method to assert with certainty whether the available set of sensors will always provide enough information to ensure that a complex and unexpected behavior has not happened in the system. The proposed method uses a publicly available model-checking tool to perform this analysis.

This paper investigates the problem of pattern diagnosis of systems modeled as bounded labeled Petri nets that extends the diagnosis problem on single fault events to more complex behaviors. An effective method to solve the diagnosis problem is proposed. It relies on a matching relation between the system and the pattern that turns the pattern diagnosis problem into a model-checking problem.

In this work we detail the design of the Interpretative Ontology used by the Dynamic Semantic Ontological Framework (MODS) proposed in [1]. We also present its use for the characterization of continuous production processes as proposed in [2], specifically for the supervision and diagnosis domain. Thus, with this ontology we can query in natural language on the Semantic Web using MODS, in the domain of supervision and diagnosis.

The chronicles paradigm has been used to determine fault in dynamic systems, allow modeling the temporal relationships between observable events to describe the patterns of behavior of the system. The chronicle recognition mechanisms used until now are semi-centralized approaches, which consist of a central component that is responsible for making the final inference about the fault diagnosis of the system, based on the information collected from local diagnosers. This model has difficulty when is implemented for monitoring very large systems. This paper proposes a fault diagnosis system based on distributed chronicles. For that, we need to extend both, the definition of the chronicles as its recognition mechanism, so that they are fully distributed. This paper presents these extensions, and then describes the fault diagnosis system based on distributed chronicles proposed, and an example of its use in SOA applications.

In classical consistency based approaches used for diagnosis of discrete event systems (DES), an inconsistency between the behaviors described in the reference model and the behavior observed through the sensors detects the occurrence of a failure in the system. If the model is not considered as error free, this traditional interpretation of inconsistencies must be enhanced to include the model errors as a potential responsible for the inconsistency. This paper has two objectives. First, it proposes a mechanism able to discriminate among inconsistencies the ones due to modeling errors an those caused by failures. Second, it describes a method to restore the consistency between the models and the observations.