Supervised machine learning (ML) has achieved significant outcomes in the industrial domain, dependent on the availability and accuracy of ground truth labels. However, the usual assumption of a ground truth to exist in the data often represents only an idealization of real-world conditions, as data labeling can be subjective and prone to errors, leading to so-called label noise. Such noise can significantly degrade model performance. Although extensive research has identified methods to improve model robustness against label noise, there is a notable absence of generic, reusable frameworks that allow industrial practitioners to systematically assess model robustness. To address this gap, we extend our previous work and propose a model-agnostic framework designed specifically for evaluating robustness against label noise. Our framework incorporates two distinct label noise perturbation mechanisms: an instance-independent symmetric perturber and an instance-dependent one. We demonstrate the utility of our extended framework through empirical evaluations on two industrial datasets using six relevant time series classification methods from the literature. The results highlight the significant vulnerability of supervised ML models to both noise types and underscore the value of our framework in uncovering these robustness limitations.

Alarm floods in industrial plants overwhelm operators by triggering numerous alarms within short time intervals, significantly complicating effective root-cause analysis. Existing causal analysis methods can support operators, but typically either neglect conditional causal relationships inherent in control loops or require manual, time-consuming extraction. This paper introduces a novel automated methodology leveraging Large Language Models (LLMs) and the logic programming language Prolog to systematically extract conditional causal relationships directly from readily available textual control narratives, textual engineering documents that describe the control system in natural language. Our logic-first approach prioritizes a thorough logical analysis of control system behavior before Prolog rule generation. Evaluations on a synthetic control system confirm accurate representation of cascade and selector control logic, demonstrating the method’s capability to reliably automate causal rule extraction and effectively support root-cause analysis during alarm floods.

Alarm floods remain a challenge in industrial operations, potentially overwhelming human operators with excessive alarm notifications during abnormal situations. To address this, alarm flood classification (AFC) methods utilize historical data to classify recurring alarm patterns automatically. However, the practical utility of these methods may be limited by potential degradation in alarm data quality, resulting from sensor faults, communication errors, or detection delays, which can substantially compromise their classification accuracy and reliability. This paper proposes a novel methodology to systematically analyze the robustness of AFC methods against realistic alarm data quality issues. We introduce four distinct perturbations: missing alarms, false alarms, delayed alarm flood detection, and alarm reordering, to replicate real-world alarm data degradation. We evaluate our methodology using a novel alarm dataset derived from the Tennessee-Eastman process, while examining the robustness of six relevant AFC methods from the literature. The results demonstrate significant variations in robustness across different AFC methods and perturbation types, providing insights into their practical reliability under various realistic scenarios.

This paper introduces a contextual, continuous risk metric for predictive safety validation in unmanned autonomous vehicles. The proposed method employs an adaptive ovoidal envelope around unmanned autonomous vehicles, dynamically adjusting based on vehicle kinematics, environmental disturbances, sensor uncertainties, and hardware reliability. The risk metric smoothly quantifies safety violations, serving as a prerequisite for generating counterfactual explanations, which could be used to avoid safety-critical situations. A preliminary simulation demonstrates the metric’s context sensitivity, continuity, computational efficiency, and predictive capability.

This paper introduces a novel alarm dataset specifically designed for the evaluation of alarm analysis methods within process plants. The complexity of industrial systems and the demands for operational safety and efficiency underscore the critical need for advanced diagnostic tools capable of handling alarm floods-situations where numerous alarms are triggered simultaneously. To bridge the gap identified in existing research regarding the availability of alarm datasets, we have developed a novel publicly available dataset derived from simulated data of a nuclear power plant. This dataset allows for a detailed analysis of alarm dynamics and enables a comprehensive evaluation of alarm analysis methods. We present a systematic methodology for generating alarm data, which involves setting alarm thresholds based on the trade-off between “false alarm rates” (FAR) and “missed alarm rates” (MAR). The dataset is employed to evaluate three existing “alarm flood classification” (AFC) methods, showcasing the practical implications and benefits of our approach. We demonstrate that AFC methods exhibit varying performances based on the implemented alarm thresholds and the quantity of available alarm data.

In this paper, we propose a novel method for enhancing the explainability of alarm flood classification results using and adapting concepts from the field of explainable artificial intelligence. Alarm flood classification methods are helpful in managing complex industrial processes; however, their predictions can be challenging to understand and justify, especially for operators without expertise in machine learning. Our proposed model-agnostic method generates counterfactual alarm floods to provide explanations for classification results obtained from any alarm flood classification model. By examining the differences between the original alarm flood and counterfactuals, we provide actionable insights for plant operators for decision-making and understanding the underlying dynamics of alarm floods. We demonstrate the effectiveness of our approach by experiments on three state-of-the-art alarm floods classification methods and an openly accessible dataset based on the “Tennes-see-Eastman” process, showcasing the added value of our method in improving the explainability and trustworthiness of alarm flood classification results.

The performance of machine learning models can be significantly impacted by variations in data quality. Typically, conventional model testing does not examine how robust the model would be in the face of potential data quality deterioration. In an industrial use case, however, data quality is a pertinent issue, as sensors are susceptible to a variety of technical and external issues that may result in poor data quality over time. In order to develop robust machine learning models, industrial data scientists must understand the sensitivity of their models against data quality issues, through the application of an appropriate and comprehensive testing solution. In this work, we propose a generic framework for systematically analyzing the impact of data quality issues on the performance of machine learning models by intentionally applying gradual perturbations to the original time series data. The evaluation is performed using a benchmark industrial process consisting of multivariate time series from sensors in a complex chemical process.

Alarm flood classification (AFC) methods are used to support human operators to identify and assess recurring alarm floods in industrial process plants. State-of-the-art AFC methods, however, show shortcomings in handling an ambiguity of the activations and order of alarms and the detection of previously unobserved alarm floods. To solve these limitations, we present a novel three-tier AFC method that uses alarm series as input. In the classification stage, a linear ridge regression classifier with a convolutional kernel-based transformation (MultiRocket) is used to classify alarm floods according to their dynamic properties. In the detection stage, a novelty detection method based on the "local outlier probability" (LoOP) is used to decide whether an unknown alarm flood belongs to a known class or a novel one. Finally, we improve the classification results using an ensemble approach. Our proposed method is compared to two naïve baselines and three relevant methods from the literature using a publicly available dataset based on the "Tennessee-Eastman" process. It is evident that our method shows the highest overall classification performance and robustness of all of the considered methods and effectively overcomes existing challenges in AFC.