Predictive Maintenance for Hydraulic Oil Rig
using MinskyTM and BrainChip’s AkidaTM (Edge Ai)
Background/Problem Statement:
Typically, Oil rigs are critical assets for the energy industry, and their downtime due to unexpected failures can lead to significant financial losses and environmental risks. Traditional maintenance approaches, such as routine inspections or reactive maintenance, are costly and often fail to prevent critical equipment failures in advance. The aim of this project is to identify the optimal time for maintenance to avoid unplanned shutdowns and reduce operational costs using our Ai enabled Engine Minsky and implement Edge Ai using Akida platform
Solution Overview:
After comprehensively analysing the data, we designed the solution by implementing our proprietary Ai Engine Minsky for predictive maintenance on hydraulic oil rig. This solution comprises of enabling real-time data integration from various sensors and historical records which consists of attributes such as Pressure, Temperature, Volume flow, Motor power, Cooling Efficiency, Cooling Power etc. Our predictive models utilized machine learning and anomaly detection techniques to analyse the data continuously. These models were designed to detect anomalies and predict equipment failures enabled proactive maintenance scheduling, reducing unplanned downtime and optimizing resource allocation. This shift from reactive to proactive maintenance not only delivered significant cost savings but also improved safety, environmental outcomes, and the overall reliability of critical assets. The case study highlights the substantial impact of AI-driven predictive maintenance in enhancing the efficiency and sustainability of industrial operations.
Typical Challenges:
- Increased downtime and unplanned outages
- Higher maintenance costs
- Accelerated asset degradation
- Safety risks to workers and the public
- Customer dissatisfaction and potential churn
- Inefficient resource allocation
- Lack of data-driven decision-making
- Missed cost savings opportunities
- Potential regulatory non-compliance
- Competitive disadvantage in the market
Results/Screenshots
Key Benefits (Minsky):
- User-Friendly cloud-based AI platform
- Scalable across various domains/data.
- Provides you a list of % dependency features that can be used to optimize the outcomes.
- Ability to fine tune or optimize the models by trying different algorithms / prediction attributes
- Easy integration with other third-party solutions such as TABLEAU for data visualization
Outcomes:
- Reduced downtime and increase in cost savings
- Improved safety to workers
- Improved Asset life Span and reliability.
- Better utilization of resources which increased productivity
