Monitoring and diagnosing complex, real-world, industrial hybrid systems require accurate and up-to-date models that can adapt to evolving system behaviors. Such systems, characterized by both continuous and discrete dynamics, are best represented by hybrid models. In this article, we present the segmentation step of HyMED (Hybrid Model Enrichment for Diagnosis), a model-based health monitoring and diagnosis method that monitors hybrid systems and automatically updates the system model if necessary. HyMED uses noisy multivariate time series data to dynamically update models, addressing unanticipated degradations and faults. A key feature of HyMED is its online and passive segmentation step (ODS), which enables robust detection of system mode changes in complex, nonlinear time series. Unlike traditional segmentation methods, ODS dynamically determines its segmentation hyperparameters through an automatic parameter selection process. ODS guarantees adaptability without the need for manual adjustment. The effectiveness of HyPED’s segmentation method is demonstrated through a case study on an engine timing system, where its performances are compared to the offline method depicted in the Ruptures library.

Abstract Anomaly detection is critical in industrial systems for ensuring equipment reliability and improving product quality, especially with the increasing complexity of electronic board production. However, traditional anomaly detection approaches often fail when dealing with high-dimensional data and limited system knowledge. To address this gap, this article aims to develop an effective unsupervised method for anomaly detection suitable for large-scale industrial contexts with minimal prior knowledge. The proposed Multi-block Local Outlier Factor (MLOF) method combines a variable decomposition technique based on Mutual Information and spectral clustering with a local anomaly detection algorithm using the Local Outlier Factor. The method was validated on the Tennessee Eastman Process and real-world industrial cases from Surface Mount Technology production lines, notably by comparing its results with 5 other methods in the literature. Results demonstrate a 15% improvement in anomaly detection performance compared to classical LOF on benchmark data and effective application in detecting anomalies in real production scenarios. The MLOF method represents a significant step forward in anomaly detection for complex systems, offering robust, scalable, and accurate solutions even in data-intensive and knowledge-scarce environments.

Structural analysis-based diagnosis allows for the extraction of a wealth of information and properties by studying a structural model that represents a physical system. This diagnosis approach is centered on structurally overdetermined sets, which enable the generation of residuals for fault detection and isolation. As the 'for Dummies' editorial collection, this article aims at taking on complex concepts and making them easy to understand. It aims to clarify and compare key concepts in structural analysis, focusing on Minimally Structurally Overdetermined (MSO) sets and Minimal Test Equation Supports (MTES). Additionally, we explain and illustrate the Dulmage-Mendelsohn decomposition, which helps identify structurally overdetermined parts of the system and plays a important role in the structural analysis process. Through detailed exploration and practical examples, we demonstrate the roles, applications, and interrelations of these sets, highlighting their respective strengths and limitations. The paper provides an overview of the algorithms used to identify and use these sets, including a theoretical and practical comparison of their computational efficiency and diagnostic capabilities.

Health monitoring of current complex systems significantly impacts the total cost of the system. Centralized fault diagnosis architectures are sometimes prohibitive for large-scale interconnected systems, such as distribution systems, telecommunication networks, water distribution networks, or fluid power systems. Confidentiality constraints are also an issue. This article presents a decentralized fault diagnosis method that only requires the knowledge of local models and limited knowledge of their neighboring subsystems. The method, implemented in the decentralized diagnoser design ($D^{3}$) algorithm, is based on structural analysis and can advantageously be applied to high-dimensional systems, linear or nonlinear. Using the concept of isolation on request, a hierarchy is built according to diagnostic objectives. The resulting diagnoser is based on analytical redundancy relations (ARRs) generated along the hierarchy. Their number is optimized via binary integer linear programming (BILP) while still guaranteeing maximal diagnosability at each level.$D^{3}$proves of lower time complexity than its centralized equivalent. It is successfully applied to a nonlinear combined cycle gas-turbine power plant.

This paper focuses on the application of a Petri Net-based diagnosis method on a planetary rover prototype. The diagnosis is performed by using a model-based method in the context of health management of hybrid systems. In system health management, the diagnosis task aims at determining the current health state of a system and the fault occurrences that lead to this state. The Hybrid Particle Petri Nets (HPPN) formalism is used to model hybrid systems behavior and degradation, and to define the generation of diagnosers to monitor the health states of such systems under uncertainty. At any time, the HPPN-based diagnoser provides the current diagnosis represented by a distribution of beliefs over the health states. The health monitoring methodology is demonstrated on the K11 rover. A hybrid model of the K11 is proposed and experimental results show that the approach is robust to real system data and constraints.

This paper presents the theoretical keystone for a decentralization of model-based diagnosis by proving the equivalence between decentralized and centralized residual generation. The proof is based on structural analysis and graph-theoretical concepts. The second contribution of this paper is the design of a decentralized fault-focused residual generation scheme advantageously implementing a strategy of fault isolation on request. Algorithms are tested on the attitude determination and control system of a low Earth orbit satellite.