What is Anomaly Detection?
Anomaly detection is the process of identifying unusual patterns in data that do not conform to expected behavior.
It is often used in workplace safety to identify any inconsistent behavior or occurrences, and to take action to mitigate risks.
Applications of Anomaly Detection in Workplace Safety
Anomaly detection technology uses real-time artificial intelligence (AI) and machine learning algorithms to automatically detect deviations.
These algorithms are trained on normal behavior so that they can identify deviations from the norm. Anomaly detection can be used to monitor employee behavior in high-risk environments, machine operations, and identify any exceptions.
The Importance of Anomaly Detection for Workplace Safety
In the context of workplace safety, anomaly detection can be used to identify potential hazards before they cause accidents.
Analyzing data from sensors, machines, and employees enables safety managers to pinpoint areas of the workplace that may be at risk and take steps to mitigate those risks.
Using Leading Indicators to Prevent Safety Incidents
This is important, as there are often leading indicators that precede safety events. For instance, what if an employee fails to wear their hi-vis correctly?
This would be considered an anomaly since all workers would be required to wear their hi-vis gloves correctly. Detecting these anomalies immediately allows the EHS team to be notified, which could avert a potential safety incident.
Improving Safety Standards Through AI-Powered Anomaly Detection
Anomaly detection is a powerful tool that can help organizations improve overall safety standards and reduce the occurrence of safety incidents.
Numerous studies have indicated that safety anomalies are often hidden in plain sight. Using AI-powered anomaly detection systems allows organizations to take a more proactive approach to worker safety instead of the traditional reactive methods.
How Anomaly Detection Works
There are two main types of anomaly detection: rule-based anomaly detection and data-driven anomaly detection.
- Rule-Based Anomaly Detection
Rule-based anomaly detection relies on safety experts to identify potential hazards and develop rules or thresholds that will trigger an alert if breached.
For example, a rule might state that an employee must not work alone in a certain area of the factory floor or that a machine must not operate above a certain speed. If these rules are violated, the system will generate an alert so that corrective action can be taken.
This rule-based anomaly detection method does not require external data and relies on expert knowledge to define detection models. However, it can be time-intensive to develop and maintain, especially for workplaces with diverse hazards.
Challenges in Implementing Rule-Based Anomaly Detection
The downside is that it can be time-consuming and expensive to develop rules for every possible hazard in the workplace.
Additionally, rules tend to be specific to individual workplaces and may need to be updated as new hazards are identified or old ones change.
- Data-Driven Anomaly Detection
Data-driven anomaly detection uses unsupervised anomaly detection techniques, such as machine learning models and deep learning, to identify unusual patterns in large data sets.
This approach enables systems to learn from historical data and adapt to changes, making it more dynamic compared to rule-based systems.
Enhancing Rules with Data-Driven Detection
Combining data analysis with rule-based systems provides additional insights. For example, let's say you have a rule that states employees must not work alone in a certain area of the factory floor.
A data-driven anomaly detection system could analyze sensor data to identify when someone is working in that area outside of normal hours or when there is no one else nearby.
Applications in Monitoring and Safety
This type of system can also be used to complement other rule-based systems, such as those that monitor machine speeds or employee fatigue levels.
Anomaly detection solutions are increasingly being used in industrial settings such as factories and power plants due to their ability to improve worker safety by identifying potential hazards before they cause accidents.
These systems are also essential for network traffic monitoring, fraud detection, and event detection in other industries.
Advantages of Machine Learning in Anomaly Detection
Data-driven detection is generally the better choice since it uses machine learning to identify anomalies and trigger alerts. This approach provides flexibility and scalability, making it ideal for real-time anomaly detection in complex environments.
Real-Time Anomaly Detection in Workplace Safety
Historically, anomaly detection was only possible by analyzing large volumes of data. That’s changing, as AI systems are capable of analyzing and identifying anomalies in real time.
As soon as an anomaly is detected, such as a breach of safety protocols in the workplace, the system immediately creates an event and informs the relevant authorities of this event.
More importantly, the system can be configured to alter the parameters of what’s considered an anomaly. This allows companies to vary the threshold of acceptable risk in different environments.
Using AI-Powered Solutions Like Protex AI for Identifying Anomalous Data
Protex AI is a workplace safety technology that uses computer vision to observe employee behavior and identify any anomalies, such as employees not wearing proper safety equipment in the correct way.
It immediately creates a notification when an anomaly is detected, informing the employee or EHS teams about the breach of safety protocols. It’s fully customizable, allowing companies to create custom safety rules based on their experience and their definition of risk.
Protex AI can connect to all modern CCTV networks, and can auto-generate reports that companies can use to better understand safety performance across multiple departments.