From diagnostics to predictive maintenance, analytics brings the peace of mind through a multifaceted approach that RF operations have long needed
The broadcast industry once seemed to live on an island of its own, amongst a vast sea of technologies and acronyms that one had to be an insider to understand. While plenty of these examples remain, the transition into the world of digital workflows and IP networks finds us swimming in the same waters as many other industries when it comes to navigating modern technology platforms.
Most technology-driven industries today embrace AI and the cloud, with innovators drawn to the former for application-driven innovation that can create entirely new operational models. The cloud offers more of an efficient approach to workload management and at a much grander scale, with the ability to move, store and manage content and information with greater flexibility than at ground level.
Analytics represents another area of explosive technological growth that perhaps is the most meaningful of these examples to businesses today. Analytics help businesses extract important information from their data, and capture meaningful insights that otherwise may stay hidden and undetected.
Analytics typically fall under four categories, all of which have a place in the broadcast universe: descriptive, diagnostic, predictive and prescriptive. Descriptive models correlate historical data with system changes, while diagnostic models analyze the reasons behind the events that influenced changes.
The crystal ball elements surfaces with predictive models, which consider potential system changes and outcomes based on historical and trending data, while the prescriptive model is the influencer of the quarter, leveraging our friend AI to stay many steps ahead of how systems should be modified for future optimization.
All four models exist to help users, including those in the broadcast industry, to run their operation more efficiently and/or identify issues that sometimes create bigger problems. For broadcasters, that can mean taking decisive action within the technology infrastructure to avoid failures that lead to financial burdens.
Analytics Enters the Broadcast Space
Analytics has been a part of the broadcast fabric for decades, though typically associated with the viewer ratings and advertising side of the business. On the broadcast manufacturing side, companies have embraced descriptive analytics to understand how to become more efficient, nimble and quicker to the marketplace.
In fact, descriptive analytics, with a pinch of predictive modeling sprinkled into the mix, provided Dielectric with the fuel to make systemic changes to its design and production process for antennas. Investments in new machinery and a refinement in design workflows followed, reducing production times and accelerating shipment dates from the FCC repack forward to today. For low-power antenna production, we have sliced machine times from three-to-five days down to two hours; for high-power side-mounted antennas, two-and-a-half weeks to two days.
Analytics has found its way deeper into the broadcast workflow over the past five to seven years, particularly as more IP-based monitoring platforms appeared on the market. With an emphasis on network based QoS and QoE monitoring, the next natural step was to build descriptive and/or diagnostic models that made it easier to connect what technical issues caused viewers to change the channel or tune out completely. Soon, predictive models found their way into the mix, helping broadcasters and service providers absorb trends over time and adjust for anomalies that would, hopefully, prevent technical issues and keep viewers tuned in.
How RF Analytics Save Broadcasters Money
The RF infrastructure is one part of the air chain where analytics has remained absent. Arguably, RF is where analytics may be needed the most, given the extent – and expense – of the damage can be done.
Dielectric took the lead several years ago with the launch of an IP-based RF monitoring system that essentially allowed broadcaster to remotely monitor the health and status of antennas, transmission line and pinpoint systemic changes caused by reflected power increases, arcs and other potentially damaging events. Same as the design workflow, analytics was the next step, and following was the 2023 launch of Apollo, an advanced analytical software suite to help users quickly analyze and formulate site data into actionable reports.
Let’s consider the traditional responsibilities of the RF engineer in keeping systems in optimal condition. The general approach was to begin with a system sweep, which would provide a stationary snapshot of how that system’s health and operating conditions appeared at that precise moment. That would be repeated in another six to twelve months in best case scenarios, and certainly provided useful information to absorb relative to potential system maintenance or upgrades that required attention. This gives the engineer the ability to compare two or three snapshots in time over the course of years. It does not address the variables that affect changes over time, from temperature swings to daytime/overnight performance – and much more.
Analytics change the game because it gives the engineer daily datasets, and the ability to see how minor degradations over time add up to push operational thresholds. For example, the transmission line is a common point of RF system deterioration that can lead to extensive, expensive damage. A broadcaster with space on a 1,000 ft. tower can establish a baseline measurement for each section of the transmission line, and leverage descriptive and diagnostic analytics over time to confirm if the line remains stable or is beginning to deteriorate due to age, maintenance needs or environmental effects. On the antenna system side, the diagnostic side may confirm that higher VSWR points to technical issues with the system, while a system arc – a spike in RF power can burn up the entire system – points to a component failure or weather-related event.
Actionable datasets without understanding how technologies perform are worth little, so a knowledge of RF passive devices is required to use RF analytics effectively. That means knowing how the transmitter, combiner, filter, switches, transmission line, antenna, elbows and auxiliary RF gear should function over time. Increasingly, analytics is being offered as a managed service, where remote technicians can monitor changes over time and add truly predictive modeling to prevent future failures, and save broadcasters a lot of money on making repairs and approving costly replacement work. It’s only a matter of time before we see more AI innovation enter the mix, bringing the value of truly prescriptive analytics into the fold.
Learn more about Dielectric, here.
