蘑菇视频

Motors, pumps, machines, heartbeats: They all create rhythms that artificial intelligence (AI) can analyze. Keep reading to discover how 蘑菇视频 Allen is using sound recordings to help the U.S. Army鈥攁nd discover how advanced machine learning applied to time series can provide remote diagnostics for an array of missions and applications.

The U.S. Army Communications-Electronics Command (CECOM) needed to reduce downtime and lower costs associated with maintaining equipment such as their workhorse Tactical Quiet Generators (TQGs), a critical source of expeditionary power for Army facilities around the world.

Warning indicators can alert operators of impending TQG problems, but they don鈥檛 necessarily specify the severity of the issue nor point to a remedy. While installing sensors could help with early warning, the necessary modifications can be costly.

The 蘑菇视频 Allen team had an idea: Can we diagnose a problem just from the sound we hear?聽鈥淚 was talking with an Army client at a facility surrounded by equipment, when the thought of using mobile phones as data collectors struck me,鈥� said Eric Syphard, chief scientist for the project. 鈥淭he client thought the idea was intriguing, so we came up with an experimental plan together.鈥�

CECOM gave our scientists access to a set of generators so they could record the sounds of their operation under varied conditions: fully functioning, cylinder malfunction, restricted airflow, etc. The team then used the data they collected to train a computer model. They also created a mobile application that allows users to upload real data for analysis.

We introduced a prescriptive maintenance mobile app, Acoustic Machine Intelligence (AMI), which provides analysis in three simple steps:

• Record 30 seconds of audio on a mobile phone such as an Android or iPhone聽
• Snap a photo of the equipment identification plate showing the model type and specifications
• Upload the audio and photo via the app聽聽

Using signal processing, deep learning, and advanced statistics, the system provides real-time fault diagnosis with accuracies near 80 percent鈥攊n seconds.

鈥淎MI increases system readiness by helping operators discover system faults in real-time so issues can be addressed before they become problems,鈥� says Eric. 鈥淎nd the app works worldwide, even in low-bandwidth environments.鈥�

Sean McKenna, a senior analytical scientist leading the development, says the AMI concept can be applied to many areas. 鈥淥ut of the box, our mobile devices measure sound and other variables such as vibration. By adding other sensors, things like oxygen and carbon monoxide could be measured. That opens the door to many uses for this technology鈥攚hether in defense, healthcare, or equipment maintenance,鈥� says Sean. 鈥淭ime series analysis is complex since you are collecting so many points every second. Individually each point is meaningless, like one note in a song. It鈥檚 the groups of points that create the beat.鈥�