Minimising false alarms in flame detectors

Kevin Killeen, global product line manager for Flame Detection at MSA Safety, outlines how advanced diagnostic technologies help minimise false alarms in flame detectors

In industrial environments where flammable materials are handled, flame detection systems are crucial for safety. They serve as an essential layer in safety programmes, helping to prevent fires and explosions by detecting the presence of flames early on. However, false alarms can be a significant issue, disrupting operations and potentially desensitising personnel to real alerts. Different flame detection methods exhibit varying false alarm profiles depending on the application, which is why advanced diagnostic technologies, like artificial neural networks (ANNs), have been developed to enhance these systems and reduce false alarms.

Traditional flame detection systems rely on sensors such as infrared (IR) and ultraviolet (UV) detectors to identify flames. These sensors, however, can be prone to interference from sources like sunlight, arc welding, and hot surfaces, which may lead to false alarms. Environmental factors like dust, smoke, and fog can further limit the effectiveness of flame detectors, making it challenging to distinguish between genuine threats and false alarms.

Addressing the challenges

ANNs are a powerful solution to these challenges. They are computational models inspired by the structure and function of the human brain, capable of learning complex patterns and making decisions based on vast datasets. When applied to flame detection, ANNs can discern subtle differences between actual flames and potential sources of interference, significantly reducing false alarms.
ANNs are trained using extensive datasets of spectral data from both real flames and common sources of interference. Through supervised learning, the network adjusts its internal parameters to optimize its ability to accurately classify input data.

Once trained, the ANN can quickly analyse incoming sensor data and determine whether a detected anomaly corresponds to a genuine flame or a false alarm. Since 2005, MSA has been at the forefront of using artificial neural networks in flame detection technology.

Numerous industries, including oil and gas, chemical processing, and manufacturing, have adopted ANNs for flame detection with remarkable results. By integrating ANNs into their safety systems, companies have reported significant reductions in false alarms, leading to enhanced operational continuity and improved worker safety.

Additionally, the scalability of ANNs allows them to be deployed in diverse environments, from offshore platforms to industrial plants, highlighting their versatility and effectiveness.

Key advantages

The key advantages of using ANNs in flame detection include:
1. Adaptability: ANNs can handle varying environmental conditions and sources of interference due to their extensive training library, making them robust in real-world applications.
2. Accuracy: ANNs leverage sophisticated pattern recognition capabilities to differentiate between genuine flames and false alarms with high precision.
3. Efficiency: ANNs can process large volumes of data in real-time, enabling rapid decision-making and minimizing response times in critical situations.
4. Reduced maintenance: With fewer false alarms, flame detection systems that incorporate ANNs require less frequent maintenance, resulting in cost savings and improved operational efficiency

The FL5000 Multi-Spectrum Infrared (MSIR) Flame Detector is our latest generation flame detector. It builds on the foundation set by the FL4000H, with increased neural network capabilities that further reduce false alarms. The proprietary MSIR flame algorithm ensures that the detector verifies the presence of a legitimate flame before initiating an alarm, protecting both assets and budget.

Additionally, the FL5000 is the first flame detector to incorporate Bluetooth technology. With the exclusive Flame Connect App, users can easily set up, configure, and download event logs from mobile devices.

Advanced diagnostic technologies, especially artificial neural networks, offer excellent accuracy and efficiency in flame detection while minimizing false alarms. By harnessing the power of ANNs, industries can mitigate risks, protect assets, and safeguard personnel. As technology continues to evolve, the integration of next-generation ANNs is poised to set new benchmarks for excellence in industrial flame detection.