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.
Anomaly detection technology uses 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.
In the context of workplace safety, anomaly detection can be used to identify potential hazards before they cause accidents.
By analyzing data from sensors, machines, and employees, safety managers can pinpoint areas of the workplace that may be at risk and take steps to mitigate those risks.
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. By detecting these anomalies immediately, the EHS team can be notified, which could avert a potential safety incident.
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. By using AI-powered systems for anomaly detection, organizations can take a more proactive approach to worker safety instead of the traditional reactive methods.
There are two main types of anomaly detection: rule-based and data-driven.
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.
The main advantage of rule-based anomaly detection is that it doesn't require any data beyond what's already available to safety experts.
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, on the other hand, uses machine learning algorithms to automatically identify unusual patterns in data.
This type of anomaly detection is often used in combination with rule-based systems to supplement existing rules with additional insights gleaned from data.
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.
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 systems 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.
Data driven detection is generally the better choice since it uses machine learning to identify anomalies and triggers alerts.
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.
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.