3 use cases for improving operations through telemetry data

Pruthviraj Phanendrakumar

Pruthviraj Phanendrakumar

A Principal Data Scientist who aligns technology with business objectives to solve complex problems and accelerate time-to-value.

Telemetry is a relatively old science booming with new possibilities. With the now commonplace Internet of Things (IoT) and abundance of sensors to monitor everything from home security to biometric data, consumers are adopting a broad use of it to simplify and manage their daily lives. But businesses have even more to gain. The data captured through telemetry offers insights into machines, giving leaders a real-time view into performance metrics. Combined with artificial intelligence and machine learning (AI/ML) and the wealth of untapped data stored within operations and along processes, teams can gain efficiencies that reduce downtime and risk while improving revenue.

In this article, we’ll cover….

What is telemetry?

Telemetry is the practice of using digitally connected sensors to record and report remote data from equipment. These sensors create what is commonly known as the Internet of Things (IoT) and, according to IDC, IoT devices are expected to generate nearly 80B zettabytes (ZB) by 2025. That’s a lot of data!

Along with colleagues such as Associate Director of Advanced Analytics Hardik Rijhwani, I use telemetry data to improve our clients’ enterprise operations. Through our data and AI services, we capture significant insights from telemetry sensors that help both technical and business teams make KPI-driven decisions faster.

How do we use telemetry data?

There are many data science and analytics challenges between collecting data and delivering actionable insights. Telemetry sensors produce an exorbitant amount of unstructured data. To structure the data, we clean, convert, contextualize and connect it to isolate what’s important and how it should be organized in accordance with KPIs.

In this stage, we apply AI and analytics tools such as computer vision, natural language processing (NLP) and graph database (graph DB) to automate and streamline the process of gleaning usable data. For example, in graph DB, we use property graph models, which are made up of nodes (data storage) and edges (the relationships between those nodes), for data analysis and data interpretation. Statistical modeling is applied to conclude thresholds and performance parts based on sensor output. Graph query language and Cypher query allow us to analyze complex, unstructured and vast amounts of data quickly and in real time.

“How we configure the system is important for data capture, Rijhwani said. “But the analysis and insights we are able to structure and pull from data is what is significant for our customers today.”

Those of us working with telemetry data and sensors are also concerned with where the intelligence is created and where decisions are made. While cloud computing is common in the field, we are continuously moving toward advanced practices with edge computing, in which decisions can be made on-site at the device. This is a developing and critical use for telemetry, as many of these sensors are attached to machines in time-sensitive situations. While we often aim to predict maintenance and avoid unexpected downtime or safety risks, edge computing can offer additional prevention by, for example, turning off a corroded valve before it breaks.

3 use cases for telemetry and AI/ML in enterprise operations

In industrial and business settings, telemetry use cases are situations where sensors are applied to specific machines to collect specific sensor data. That information helps teams in a variety of industries—including high tech, oil and gas, supply chain and manufacturing—understand how those machines are performing. Here are three use-case examples:

1. Faster and remote performance computer troubleshooting with customers

Manufacturers of high-tech equipment, such as computers, will install connected sensors into their hardware that log performance metrics. The information can then be used in several ways.

“We work with one global computer manufacturer who makes these logs available to support agents,” Rijhwani said. “In the past, an expert may have had to physically see the machine to fix it. Now, when a customer has an issue, those agents can remotely access the information in real-time to troubleshoot.”

Rijhwani said it also helps product teams understand product quality. They have access to logs from hundreds of thousands of assets of the same model sold. With AI/ML, it is possible to analyze many thousands of data lines automatically and rapidly for errors, such as the machine overheating, to improve the next model.

This information also benefits account managers, who can be better prepared to help customers and drive more positive relationships in the process.

2. Predictive equipment monitoring for efficiency and safety

Whether in high tech, energy or product manufacturing, telemetry connects and coordinates heavy machinery and equipment to help teams plan maintenance and avoid unscheduled downtime. The practice enhances safety measures while improving a company’s bottom line.

In industrial settings, such as oil refineries, there are hundreds of thousands of machines to be monitored. Whenever a machine goes down, it leads to revenue loss. It also increases risks to employee safety and even the environment.

Companies use telemetry to monitor the health of the system and prevent unplanned downtime. AI/ML analysis of collected data helps the teams understand the conditions that put those machines at risk. It can also alert teams to those conditions and set up a regular preventative-maintenance schedule to prevent an incident from occurring. By attaching sensors to equipment, teams can proactively schedule maintenance rather than react to a broken machine.

3. Improved shipping logistics efficiency by attaching sensors to trucks to gain insights into travel times, safety and more.

“We can use historical data to make decisions, such as suggesting better routes or scheduling preventative maintenance on fleets,” Rijhwani said. “The sensors may also improve safety by monitoring driving speed or sudden changes, such as a swerve. The information may suggest a driver is tired or distracted and sound an alarm to signal them to take a break.”

Additionally, autonomous cars and trucks being developed now are at the forefront of edge computing, where a split-second decision by the machine can have far-reaching consequences.

Not only can this data impact individual safety and efficiency; it can help improve a logistics system holistically. It helps teams analyze the whole of fleets and their movement to recommend new routes that streamline delivery or even decide whether to use different modes of transportation.

How a top oil and gas industry company uses telemetry

According to leaders at Shell, a top global oil company, the company monitors its thousands of miles of pipeline, up to 10,000 valves and a million instruments with embedded sensors. As an industry innovator, Shell has pursued digital transformation through telemetry, AI/ML and data science. They have succeeded in reducing downtime, improving safety and protecting their margins in the process.

Aligned Automation: Your partner in digital transformation

Enterprises are facing an explosion of data and feeling the pressure to use it strategically to streamline operations. At Aligned Automation, we combine innovative functional knowledge with dedicated technical experts in data, process and ML/AI services, accelerating your journey to intelligent operations. From enriching, structuring and connecting disparate data to enabling advanced technology, we work alongside enterprise teams to realize ambitious business goals.

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