Case Study: Nippon Gases prevents unplanned downtime with SymphonyAI Predictive Asset Intelligence

Nippon Gases prevents unplanned downtime and extends its APM strategy with Predictive Asset Intelligence

Nippon Gases, a leading industrial and medical gas supplier operating across more than 13 countries, needed to improve plant reliability and reduce unplanned downtime for its production-critical and safety-critical equipment. While the company already used traditional condition monitoring methods such as vibration and oil analysis, those tools did not cover all failure modes, creating a need for a more advanced predictive maintenance approach from SymphonyAI.

SymphonyAI implemented Predictive Asset Intelligence, built on the IRIS Foundry industrial DataOps platform, to provide real-time asset health monitoring and AI-driven anomaly detection for critical equipment. The solution identified significant anomalies that traditional monitoring missed, helping Nippon Gases prevent unplanned stoppages and extend its APM strategy, while also making asset management more accessible through a no-code interface for broader team use.

Nippon Gases

Ben Engels

Reliability Manager