Industrial process safety systems are predominantly reactive: alarms activate after limits are crossed, faults are diagnosed after deviations develop, and HAZOP knowledge remains offline during operation. This paper proposes Predictive Operational Safety Engineering (POSE) as an emerging research paradigm in which operational safety is treated as a continuously forecastable state rather than a post-event classification, shifting the operational question from what has gone wrong? to how much safe operating time remains, and which intervention is most urgent? Four integrated predictive safety metrics anchor the framework: Remaining Safety Margin (RSM), quantifying the normalized distance between the predicted process trajectory and the nearest safety boundary; Remaining Safe Operating Time (RSOT), estimating when that boundary will be crossed under the current trajectory; the Operational Vulnerability Index (OVI), combining margin depletion rate, safeguard availability, and consequence severity into a single intervention-urgency signal; and Predictive Safety Confidence (PSC), the probability that a specific named operator intervention can be executed to completion before the predicted safety boundary is crossed, coupling prediction uncertainty with action execution time. The Predictive Operational Safety Twin (POST) is proposed as a three-layer reference architecture implementing POSE through predictive process intelligence, predictive safety intelligence, and human safety intelligence. The paper synthesizes six research streams, positions POSE against seven adjacent disciplines, states ten guiding principles, and formulates a research agenda. As a conceptual narrative review, the paper does not claim empirical validation of POSE. Instead, it establishes the foundational vocabulary, reference architecture, and research agenda required to advance predictive operational safety from an emerging concept toward benchmarked and industrially validated practice. This article constitutes the conceptual and evidence-synthesis phase of a staged research program; subsequent work must test the proposed constructs through benchmark simulation, uncertainty calibration, baseline comparison, operator studies, and industrial case studies.
The complexity of oil and gas operations has gone up, so the demand for intelligent and proactive safety management systems also went up, to reduce operational hazards, equipment failures, and those really bad industrial catastrophes. Traditional Safety Instrumented Systems (SIS) mostly rely on static rule-based designs and fixed threshold mechanisms, but they dont really have adaptive intelligence or predictive decision-making when the operational situation changes quickly. In the last few years, the integration of AI, IIoT, machine learning, and predictive analytics has been changing both industrial process safety and operational reliability in parallel. This study presents an AI-Driven Framework for Predictive Safety Performance in Intelligent SISs specifically for oil and gas facilities. The goal is to make hazard prediction more reliable, support defect detection, enable real-time monitoring, and enable autonomous safety decisions. The work tries to build and test a smart predictive safety framework which can identify abnormal process conditions, anticipate equipment failures, cut down false alarms, and strengthen safety during hazardous industrial scenarios. Industrial safety engineering principles are combined with AI-based predictive analytics, using Random Forest, Support Vector Machine (SVM), Artificial Neural Networks (ANN), Gradient Boosting, and Long Short-Term Memory (LSTM) networks. Before model training and testing, preprocessing industrial datasets from sensors, PLCs, SCADA systems, DCS, and IIoT-enabled monitoring devices is done, including normalisation, anomaly labelling, feature extraction, and noise reduction. After that, several industrial hazard simulations evaluate the framework, such as gas leakage, pressure escalation, fire dangers, valve malfunction, and pipeline collapse. The experimental results show that the proposed AI-driven SIS framework outperforms traditional SIS architectures in fault detection accuracy, response latency, false alarm generation, system reliability, and risk mitigation. LSTM had the highest predicted accuracy among AI models because it could analyse temporal industrial process data. Per the study, AI enabled predictive intelligence in industrial safety systems supports more or less autonomous decisions, predictive maintenance, and adaptive hazard management, in modern oil and gas facilities. The framework encourages intelligent industrial safety ecosystems that boost operational resilience and equipment reliability, as well as sustained safety performance.
Unknown authors· International journal of com...· 0 citations
Understanding how complex human, environmental, demographic and operational factors interact to elevate the probability of safety infractions is essential for developing predictive safety systems in high-risk environments such as highways. This study proposes a data-driven framework for identifying empirical inflection and threshold operational points, whereby risk shifts abruptly from acceptable to hazardous levels. In addition, this study develops interpretable, rule-based triggers for proactive safety interventions in highway maintenance and traffic management.
Using a synthetic dataset (backed by a positivist philosophical stance) reflecting safety-based variables recommended in the literature (e.g. human, operational, environmental stressors and organisational conditions), a supervised machine learning model (i.e. Random Forest) was trained to estimate infraction probabilities. A threshold discovery algorithm was then implemented, combining bin-wise probability estimation with prominence-based inflection detection and rule induction to extract applicable safety triggers. Feature importance measures were used to contextualise the relative influence of predictors on the model's risk output.
Results revealed clear, interpretable thresholds across multiple predictors, including sharp risk transitions for consecutive workdays (3–4 days), fatigue level (=5), sleep duration (<6 h), traffic density (>600 vehicles/hour) and physiological stress (>90 bpm). Non-linear variables such as cognitive load and training quality exhibited oscillatory risk patterns yet still produced meaningful inflection points. Based on the discovered thresholds, safety trigger rules were formulated to aid in safety management decision-making.
This study contributes a novel, transparent methodology for threshold-based risk detection, bridging machine learning interpretability with practical safety management. The proof-of-concept model developed demonstrates how inflection-point analytics can support early warning systems, personalised interventions and data-driven policy design in safety-critical operational settings.
L. Bortey, David J. Edwards, I. Rillie· Smart and Sustainable Built...· 0 citations
This review presents a comprehensive analysis of industrial hazard identification and automated safety control systems by synthesizing research published between 2013 and 2026, and explores the role of Safety 4.0 and Industry 5.0 paradigms in promoting human-centric, sustainable, and resilient industrial operations.
T. M. Hossain, M. Chowdhury, K. M. M. F. Mithon et al.· International Journal of Mul...· 0 citations
Aviation safety improvement remains a continuous engineering challenge because modern aircraft are sensor-dense, software-intensive, and operationally interconnected, while health and operations data often remain fragmented across onboard, maintenance, dispatch, and ground systems. This fragmentation can delay weak-signal recognition and coordinated response. This paper proposes AI-SAFE FlightNet, a Safety-Aware, Intelligent, Federated, Explainable Flight Automation Network that integrates aircraft data intelligence, onboard edge AI, subsystem digital twins, phase-aware predictive analytics, evidence-grounded coordination, ground operations support, federated fleet learning, and governance by design. The framework supports pilots, engineers, dispatchers, and maintenance controllers without replacing certified human authority or certified aviation systems. A formal model combines sensor, flight-phase, environmental, and maintenance-history variables to estimate risk trajectories, prioritize alerts, and generate auditable response packages. The proposed evaluation uses public engine-degradation and trajectory datasets, synthetic subsystem telemetry, simulated maintenance records, and digital-twin experiments. Expected contributions are earlier warning, improved maintenance readiness, faster aircraft-to-ground coordination, privacy-preserving fleet learning, explainable recommendations, and measurable operational risk reduction. The objective is improved resilience and decision support, not zero risk or unrestricted autonomous control.
Prudvi Saisaran Ponduru, Pavani Priya Vyshnavi Nandanavanam, S. Ponduru· Engineering and Technology J...· 0 citations
Deploying Machine Learning (ML) models in safety-critical domains requires connecting their behavior to safety engineering standards such as IEC 61508. These standards focus on mitigating systematic failures in software, yet ML models also exhibit failures that are better treated as random and thus fall outside this scope. Uncertainty quantification can characterize such failures through a per-prediction uncertainty score, but a score alone does not determine whether an output is safe; this requires an additional step, referred to as uncertainty handling.This work introduces Uncertainty Gate, an uncertainty-handling framework that is agnostic to both the underlying ML model and the source of uncertainty. The framework treats the uncertainty score as an online diagnostic that accepts or rejects each prediction against a threshold. It then connects the resulting risk to IEC 61508 through a finite-sample bound, which accommodates dependent (non-i.i.d.) test data via a proper cover number. This allows practitioners to determine whether a given dataset and model meet a target Safety Integrity Level (SIL), and how much test data such a guarantee requires. We demonstrate the framework on a worked example targeting SIL 1 and discuss its use cases, extensions, and limitations.
Mateus Molina, Alexander Günther, Peter Liggesmeyer· International Conference on...· 0 citations
Ensuring reliable equipment operations are critical for production efficiency and safety compliance in manufacturing industries. Unexpected machine breakdowns not only disrupt operations but also increase maintenance costs and safety risk. Traditional approaches — whether reactive or preventive — often fail to incorporate real-time equipment data and often overlook early fault indicators. Predictive maintenance strategies based on artificial intelligence models, address these shortcomings by monitoring machine conditions in real time, detect anomalies and forecast failures. Thus, this study proposes a refined deep neural network to classify machine health and estimate failure probability, thereby emphasizing Remaining Useful Life (RUL) prediction as a strategy for optimizing maintenance scheduling. In this study, the AI4I 2020 Predictive Maintenance dataset, which contains 10,000 records of machine operating conditions, including air and process temperatures, rotational speed, torque, tool wear, product type, and failure status was used for the evaluation of the proposed model. To improve the proposed model's performance, detailed preprocessing steps were deployed on the dataset. These steps include some preliminary categorical encoding and feature standardization on the dataset while class weighting, SMOTE and focal loss were deployed for handling class imbalance issues. According to the results achieved, the proposed model performed better across all metrics considered in comparison with similar models including baseline models. By integrating data-driven AI techniques with predictive maintenance strategies, this research study demonstrates how manufacturing plants can reduce downtime, extend machine lifespan, and minimize unnecessary maintenance interventions.
O. A. Alimi, O.C. Tshidavhu, N. Khanyile et al.· 2026 6th International Confe...· 0 citations
