If your company has connected assets deployed in the field, such as measuring instruments or machinery equipment with sensors, your priority is undoubtedly to keep those assets running around the clock. While IoT (Internet of Things) solutions connect assets to a centralized platform to track and manage them, they don’t have the capability to predict when an asset might fail. IoT solutions can tell you how an asset is currently performing, but not what might happen in the near future.
Imagine receiving a Slack notification alerting you that an asset is showing signs of early failure. This would allow you to address the issue proactively before the failure happens. For example, an HVAC company could notify a cold storage warehouse that a chiller needs service ASAP before it fails and the stored inventory goes bad, completely avoiding a very costly failure.
But how is this possible? What technology is needed to predict when connected assets need service or are underperforming?
AI to Detect Unusual Performance Patterns
AI continues to make life easier for many professions, from speeding up code creation to summarizing Zoom meetings. It enables companies to do more with fewer people and advances the possibilities of what can be done with collected asset data. However, companies with massive amounts of connected assets have typically struggled to monitor and maintain all their assets effectively. This is where anomaly detection comes into play.
AI-Based Anomaly Detection 101
Anomaly detection identifies patterns in data that don’t conform to expected behavior. Anomalies may indicate critical incidents, such as equipment failures, security breaches, or system inefficiencies. Traditional methods of anomaly detection, using statistical methods and threshold-based alerts, often led to high false-positive rates and missed detections. Newer AI-based anomaly detection is more dynamic, accurate, and scalable, identifying irregularities in real-time. AI leverages sophisticated algorithms and machine learning models that are exponentially more accurate than traditional methods.
Fundamentals of AI-based Anomaly Detection
Anomaly detection involves identifying data patterns that deviate significantly from established norms, signaling potential failures, security threats, or operational inefficiencies. With AI, anomaly detection processes in IoT environments have become more advanced, accurate, and adaptable. AI models for anomaly detection are trained on historical data to learn what constitutes normal behavior within a system. Once trained, these models can identify outliers or anomalies in new data, providing real-time insights crucial for maintaining system integrity.
Types of Anomaly Detection Techniques
Anomaly detection techniques in AI can be broadly classified into three categories:
- Supervised Anomaly Detection: Requires labeled datasets where anomalies are identified during training. The model learns to distinguish between normal and abnormal patterns. This technique is powerful but limited by the availability of accurately labeled data.
- Unsupervised Anomaly Detection: Common in IoT applications due to the vast amount of data and the impracticality of labeling it all. This technique assumes anomalies are rare and different from the normal pattern. Algorithms like k-means clustering, autoencoders, and one-class SVM detect outliers without prior labels.
- Semi-supervised Anomaly Detection: Uses a small amount of labeled data to guide the learning process applied to a larger unlabeled dataset, enhancing the model’s accuracy and reliability in detecting subtle anomalies.
Now that we’ve covered exactly what anomaly detection is, let’s dive deeper into how any company with connected assets could benefit from AI based anomaly detection.
Anomaly Detection to Advance Every Aspect of Business
Providing proactive service is just one benefit we’ve mentioned, but using AI to detect anomalies offers many additional advantages that can significantly impact a business’s bottom line. Chances are, you haven’t considered all of these benefits, and some might surprise you.
Benefits of AI Based Anomaly Detection
Scalability
AI anomaly detection can efficiently process and analyze vast amounts of connected asset data. Countless assets are monitored in real-time, and predictions occur around the clock to find unusual patterns within one asset or an entire region of connected assets.
Example: A large manufacturing company uses AI-based anomaly detection to monitor thousands of water testing instruments installed at a growing number of water treatment facilities around the globe. AI analyzes data to raise alerts when a water quality issue is likely, helping avoid major disruptions in water supply.
Accuracy
Advanced AI algorithms minimize false positives and false negatives, ensuring businesses can reliably identify genuine issues with their connected assets without unnecessary alerts.
Example: An ATM manufacturer uses AI anomaly detection to monitor ATMs nationwide. AI predicts when cash levels will be too low, allowing technicians to service them promptly, improving customer satisfaction.
Adaptability
AI models adapt to new patterns and continuously improve their predictions as more data becomes available, remaining effective even as business environments evolve.
Example: An energy company uses AI anomaly detection to monitor wind turbines. The AI system adapts to new weather patterns and turbine behaviors, improving its ability to detect anomalies, enhancing output and profitability.
Proactive Service
AI models advance to anticipate the likelihood of failure and prescribe preventive measures, helping businesses avoid downtime and maintain seamless operations.
Example: A utility company uses AI to predict faults in the electrical grid, enabling preventive maintenance and avoiding power outages, ensuring continuous power supply.
Real-Time Processing
AI enables real-time analysis of data streams, providing immediate alerts and enabling swift responses to potential issues.
Example: A cold storage logistics provider uses AI to track perishable goods in transit. AI analyzes temperature and humidity data in real-time, alerting the provider if conditions deviate, allowing quick corrective actions to prevent spoilage.
Autonomous Response Systems
AI can not only identify problems but also autonomously respond to them through automated workflows, significantly reducing response times and operational disruptions.
Example: A pacemaker manufacturer uses AI to monitor pacemakers and autonomously initiates corrective actions, such as firmware updates, upon detecting potential irregularities, improving patient outcomes.
By leveraging these AI-based techniques, companies with connected assets can not only anticipate and mitigate risks but also enhance operational efficiency and service reliability. This strategic use of AI ensures that businesses can maintain a competitive edge by optimizing the performance and reliability of their connected assets.
Where to Get Started with AI Based Anomaly Detection?
Successfully deploying AI-based anomaly detection requires a strategic approach encompassing data management, model selection and training, and system integration.
Data Management
- Data Collection: The first step in implementing AI-based anomaly detection is to ensure comprehensive and continuous data collection across all relevant connected assets. This includes identifying the types of data that are most indicative of performance and potential issues, such as temperature readings, operational speeds, or energy consumption.
- Data Preprocessing: Once data is collected, it must be cleaned and normalized to ensure consistency. This process includes handling missing values, removing outliers, and scaling or transforming data as necessary to fit the requirements of the AI models.
- Data Storage: Efficient data storage solutions are crucial for handling the large volumes of data generated by connected assets. This involves choosing between on-premises storage, cloud storage, or hybrid solutions based on factors like data accessibility, security requirements, and cost.
Model Selection and Training
- Choosing the Right Model: The selection of an appropriate AI model is critical and depends on the specific characteristics of the data and the type of anomalies to be detected. Models like LSTM networks are suitable for time-series data, while autoencoders might be better for detecting complex patterns in high-dimensional data.
- Training and Validation: AI models require training with historical data to learn what constitutes normal behavior. It is essential to use a comprehensive dataset that includes examples of past anomalies to train the models effectively. Validation should be performed using separate data to ensure the model accurately identifies anomalies without overfitting.
- Continuous Learning: Anomaly detection models should be updated regularly with new data to adapt to changes in the IoT environment. This includes retraining models with recent data or employing techniques such as online learning, where the model updates continuously as new data comes in.
System Integration
- Integration with IoT Systems: AI models must be seamlessly integrated into existing IoT infrastructures. This requires configuring IoT devices and networks to feed data directly into the anomaly detection system and ensuring that the system can communicate findings back to the IoT management platform.
- Real-time Analysis and Response: For many IoT applications, the ability to detect and respond to anomalies in real-time is crucial. This requires setting up a robust infrastructure that can process data streams efficiently and trigger alerts or automated responses without significant delays.
- User Interface and Reporting: It’s important to provide an intuitive user interface that allows non-technical stakeholders to monitor the system, understand anomalies when they occur, and access detailed reports for further analysis. This helps ensure that the benefits of AI-based anomaly detection are fully realized across the organization.
Ready to enhance the performance and reliability of your connected assets with AI-based anomaly detection? Contact Bolt Data today. Our IoT experts have the experience to help your company get started leveraging connected asset data.
About the Author
Dr. Pankesh Patel is the Engineering Lead at Bolt Data and spearheads the architecture design for IoT and AI solutions, cloud infrastructure, and the development of secure, scalable software. Dr. Patel is a respected voice in the industry with over a decade of experience in IoT with specialized experience in smart manufacturing and healthcare.
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