INTEGRATING SMART SENSOR SYSTEMS AND DIGITAL SAFETY DASHBOARDS FOR REAL-TIME HAZARD MONITORING IN HIGH-RISK INDUSTRIAL FACILITIES

Abstract

This quantitative study examined the performance impact of integrating smart sensor systems with digital safety dashboards for real-time hazard monitoring in a high-risk industrial facility. The study adopted a facility-based observational design using system-generated monitoring records rather than survey data. A total of 3,600 monitoring episodes were analyzed, representing multiple facility zones, operational shifts, and hazard modalities, including gas, thermal, vibration, and pressure monitoring. Independent variables captured system configuration characteristics such as sensor coverage density, network protocol type, and dashboard update interval, while dependent variables represented monitoring performance outcomes, including detection latency, alarm accuracy, threshold exceedance frequency, and operator response time. Control variables accounted for operational context, including facility zone type, environmental baseline variability, equipment age category, and shift category. Descriptive results showed that sensor coverage density averaged 6.8 sensors per zone, dashboard update intervals averaged 4.6 seconds, and detection latency averaged 6.9 seconds with a standard deviation of 2.5 seconds. Alarm accuracy demonstrated a high mean value of 84.3%, while threshold exceedance frequency averaged 3.7 events per shift, indicating heterogeneous hazard dynamics across operational conditions. Operator response time, measured for episodes with valid interaction logs, averaged 18.6 seconds, reflecting variability in human-system interaction across shifts and zones. Reliability testing confirmed acceptable to good internal consistency for composite constructs, with Cronbach’s alpha values ranging from 0.76 to 0.88. Regression analysis revealed statistically significant relationships between system design variables and monitoring performance outcomes. Increased sensor coverage density was associated with reduced detection latency (B = −0.42, p < .001) and improved alarm accuracy (B = 1.12, p < .001). Wireless network protocols were associated with higher detection latency (B = 0.78, p < .001) and lower alarm accuracy (B = −2.46, p < .001) compared with wired configurations. Longer dashboard update intervals were linked to increased detection latency and reduced alarm accuracy, while also contributing to longer operator response times (p < .05).