Ask any broadcast engineer what they do, and they’ll probably tell you they “keep things on the air.” Yes! For most of us, our priority is to fix things quickly when they break.
But things are changing; we are in a fast-paced, advanced world. Now we deal with a mix of old and new technology, increasingly complicated networks, remote sites and different ways to send out content. Plus, our teams are getting smaller.
So, we must think: What if we could stop problems before they even happen?
Beyond the break-fix
Traditionally, what we do is wait for something to break and then fix it. And it has worked for a long time.
That’s still important. But our equipment and systems are so much more complicated now. It’s getting harder to fix everything just by reacting when things go wrong.
We haven’t implemented an AI-based monitoring system in our station or transmitter site yet, but I’m curious about how these systems work. AI can definitely show us patterns, because it is learning each and every piece of data coming from the system and can predict a future failure, especially for devices like a transmitter or a generator, or even looking into the logs of various devices to see how they are responding for a while.
That’s the part a machine can do faster than a human. This doesn’t mean AI can replace what we do, but it can help us notice what we might miss.
As more of our infrastructure moves into IP, the amount of data we deal with has grown significantly. Logs, packet flows, latency and device health — there’s no shortage of information. The challenge is figuring out what logs and information matter in real time.
I’ve seen this multiple times during network issues. One problem shows up, and then suddenly alerts start popping up in multiple systems. At that point, you’re not just fixing an issue; you’re trying to figure out where it actually started by going through multiple logs.
This is where AI-based monitoring seems promising. Instead of going through everything, which can be time-consuming, these systems try to correlate events and highlight what might be the root cause. That doesn’t remove the engineer from the process. But help us to make troubleshooting more focused and makes life easier.
As I said, AI can surface signals. Engineers still decide what those signals mean.
Practicality and the human role
Most modern monitoring tools have the ability to pull all available data, like equipment status, network logs and sensor data, and show it in one dashboard.
So the real value in this is that it will help reduce significant time in our weekly checks, and it will also add another layer of visibility by constantly monitoring these values and learning patterns and drifts and can predict to engineers if there is any unusual behavior. Still the same goal we’ve always had: Prevent issues before they affect the air. Another level of fault prevention.
Even though there is still some hesitation to use AI, it is valid for sure. AI can process large amounts of data quickly, but what it can’t do is understand the context. In a broadcast environment, that context matters a lot. A signal drop might look like a failure in the data. In reality, it could be a planned weekly test, maintenance work or a known condition. That’s where engineering judgment comes in, and AI may lack it.
AI can give us better information about systems when making decisions. But the ultimate decision should come from a human.
Where to start?
As I am writing about this, I’m not using any of these tools. I am undertain where and how we can use this to make systems better. If I am to start, it will probably be in one area that already causes recurring issues — a transmitter site or network segment or a critical link. The focus would be simple: Collect better data, watch trends over time, and look for changes, not just failures.
I welcome comments about your own experiences; send them to [email protected] with “Letter to the Editor” in the subject field.
Nowadays, even basic monitoring tools are starting to include some level of anomaly detection. That alone could reduce a lot of issues we deal with every day.
At the end of the day, this isn’t a shift away from what we do or from human to AI. It’s just AI acting as a catalyst for engineers to make their work and decisions easier. Our goal remains the same: Keep the systems running, stop failures before they hit the air, and protect the broadcast.
Related: Read the Radio World ebook “Optimize Your Air Chain” at http://radioworld.com/ebooks.
