AI and Machine learning is simplifying and improving business processes across industries, and asset maintenance is one of them. Companies with expensive assets in remote locations, including electrical poles, pipes, and windmills, perform routine maintenance and checks on these assets to ensure longevity and efficient performance. These inspections are mostly human-driven and are subject to inconsistencies in results.
Organizations across oil & gas, utilities, energy, telecom, and others are trying to achieve minimal asset breakdowns and are finding the answer in AI-Driven predictive maintenance.
Shifting from Reactive to Predictive Asset Maintenance
According to a McKinsey report, predictive asset maintenance can help reduce asset and machine downtime by as much as 50% and increase the life of assets by almost 40%. It can help businesses achieve:
- Enhanced equipment efficiency
- Reduced maintenance costs
- Minimized risks
However, the heart of any good AI/ML algorithm for predictive asset maintenance lies in training datasets (annotated or labeled data). The increasing demand for data annotation drives the need for annotation tools and professional data annotators. However, domain-specific datasets availability is scarce, and there is a lack of resources experienced in asset inspection data annotation.
This blog will focus on predictive maintenance models, how data annotation impacts their performance, and cover a few use cases in predictive asset maintenance.
Predictive Maintenance Models Need High-Quality Data
The estimated cost of corrosion in the oil and gas industry is around $300 billion per year globally. Corrosion is just one scenario that requires regular inspection, proving that the economic impact of these assets not working is greater than the cost of checks or maintenance.
Predictive maintenance utilizes condition monitoring through real-time images or videos captured by drones or helicopters. Predictive maintenance algorithms can analyze this data, identify potential issues and regulate them before repercussions.
High-quality data is crucial for developing effective predictive maintenance models, as it provides the foundation for identifying potential issues and making accurate predictions. Although captured easily, data can be inconsistent or incorrect, leading to inaccurate predictions and missed opportunities.
So, how can companies ensure they have access to high-quality data? The answer lies in robust data annotation and labeling.
We quickly defined guidelines, listed all possible edge cases, and built an understanding of the tasks and sub-tasks to move forward.
How Data Annotation Improves Predictive Asset Maintenance Models
The first step is collecting data from various sources such as databases, sensors, SCADA systems, ERP systems, etc. Once collected, the data must undergo sorting, cleansing, and preparation before being fed to the machine learning model. This ‘preparation’ stage involves robust data annotation and data labeling. By adding meaningful context to raw data, data annotation can significantly improve the quality of the data used and increase the accuracy of predictive maintenance models. For example, corrosion detection models may fail when the following nuances are present in the dataset:
- Assets are in different colors like red, brown, or black, making it hard to distinguish rust from paint.
- The time of day when images are taken also affects the model, as shadows make it difficult to find the rust.
- Distance from the camera when drones or helicopters take pictures of assets is high, making images grainy and unclear.
Identifying ground truth can be challenging in the above cases, requiring expert data annotators to add labels in the images or videos for the predictive asset maintenance model to produce accurate results.
5 Data Annotation Use Cases for Predictive Asset Maintenance
From asset inspection to safety tracking, let us explore five data annotation use cases for predictive asset maintenance models.
The first step to implementing an optimal predictive maintenance program is classifying assets based on criticality. Data annotation can enable asset detection and classification, enabling the ML model to identify and predict their future maintenance needs, such as repair or replacement, electrical pole type classification, component identification, and state detection.
Corrosion and Oil Detection
Asset inspection models may require data that is captured, annotated, and classified into different levels of corrosion (such as low rust, medium rust, and high rust) and oil accumulation. This information enables the ML models to make predictions and prioritize maintenance accordingly.
Our team can enhance labeling throughput and accuracy by leveraging advanced corrosion models and creating an optimized feedback loop by turning labeled images into the model training dataset.
Fracture and Crack Detection
A crack or damage in an operational civil infrastructure should ideally be detected and isolated as soon as possible to avoid further damage progression, economic loss, and loss of precious human lives. Our data experts capture and identify fractures and cracks at the pixel level across multiple degradation labels and advance AI-based predictive maintenance.
Roof Damage Detection
Different industries may require roof damage detection to prevent safety hazards, structural damage, and financial losses caused by leaks, cracks, or other damage to the roof. Our expert annotation team analyzes buildings with various levels, intensities, and types of damage, such as cracks, missing or broken shingles, punctures, and other forms of visible damage.
On-Site Worker Safety Identification and Tracking
Our data experts can identify and classify worker safety infractions for actionable intervention and prevent future incidents. With video tracking and interpolation of workers and dangerous scenarios, the ML model can assess worker clothing and compliance with safety standards, track worker movements in danger zones, and monitor harmful situations for potential hazards.
We discussed the importance of high-quality data and robust annotation to build effective predictive maintenance AI models and a few areas where data annotation is critical for improving the model. At iMerit, we leverage a combination of the right technology, technique, and talent to achieve high-quality data.