Objective To record the operational data through the crane Safe Load Indicators to Identify the risk in the Behavior of crane operators, to improve Safety standards and the need for (individual) basic training in offshore crane operation. Process The Data Logger system link with the Radio frequency identification (RFID) tags, which enable the Datalogger to record operation parameters in relation with the crane operator. The data is downloaded to an internal SD card or can be transferred to a Laptop computer for analysis using special software. The data is presented in the form of graphical charts based on the operator's ID. The collected data shows the actual lifting performance and highlight the violation for each alarm under Operator ID. It will help in identifying the weak crane operator’s weaknesses and the points required training to improve his behavior. The system will assist supervisor’s in controlling risk takers behaivior.

In ADNOC Oil and Gas 4.0 mission, we are committed to empower people with the needed capabilities and Artificial Intelligence (AI) technologies to fuel innovation, efficiency and more importantly achieve and sustain a 100% HSE, by transforming the way of handling HSE events by moving from reactive to proactive approach. The ultimate objective is to save lives, empower the vessel Captains to immediately identify and respond to violators, improve the HSE culture of the crew, and automatically generate live data analytics and statistics with the aim of improving safety in operations. The implemented AI use cases are; deviation for not wearing Protective Safety equipment in designated areas, violation of not utilizing safety passages, alert when no watchman in muster station, alarm when man overboard incident, alarm when man fell on stairs, and live Personnel on board each weather-deck. When introduced the Artificial Intelligence cameras, our marine vessels will adopt a smarter automated response and reporting culture, which will in turn, lead to increased safety oversight of our critical offshore operations. Therefore, with the advent of the AI technology, many common business processes have been automated thus enabling personnel to increase their focus on more important tasks while technologies like the AI System can handle many of the time consuming tasks. The solution components consists of Artificial Intelligence platform, high definition cameras, local server, wide-range WiFi access point, network infrastructure and a tablet. On the tablet device, the captain have full coverage of the vessel weather decks, working areas and restricted zones with a feature to generate alerts when detecting an emergency situation. This was provided to empower the vessel Captain to acknowledge and respond to violations as well as take a proactive action to prevent incidents from happening. The Machine Learning algorithm has been trained on actual scenarios and will be continuously improved by adding more recorded event to retrain the initial model. Currently, the prediction model is performing on the vessel operation mode and recording events with high rate of accuracy. In case of automatically detecting an alerting or non-compliance event, the captain would be notified, beacon lights and sound, and log recorded in the local and central system with a photo and a short video clip of the incident. The process of identifying HSE deviations are becoming digitally transformed by deploying AI capabilities on real-time video streams. The AI-based camera system leverages Computer Vision features that enables machines to get and analyze visual information and take action. The whole process of identifying HSE violation events has been digitally transformed by deploying an artificial intelligence solution to perform real time video analytics.