Manufacturing AUTOMATION

IoT and big data: The predictive maintenance silver bullet?

November 10, 2016
By Patrick Zirnhelt IFS North America

Nov. 10, 2016 – According to IDC research, the installed base of worldwide Internet of Things (IoT) endpoints will grow from just less than 13 billion units in 2015 to 30 billion by 2020.

This article looks at the potential of leveraging data produced by IoT-enabled devices to unlock dollars on the bottom line, as well as the immediate benefits IoT and big data will provide for three key industries: manufacturing, transport and utilities.

Today the ‘big’ deal about IoT is scale. Technology today doesn’t just count the number of punches or laser cuts a machine does — IoT takes those and much more to a far larger scale. Sophisticated, IoT-enabled equipment today might have dozens, or even hundreds, of sensors continually monitoring the performance of equipment components, such as lasers, cutters, grinders, accelerometers. Multiple sensors will also monitor operating conditions such as temperatures, humidity, weights, densities, flow and vibrations.

The volume of information gathered in real-time about equipment performance is higher than ever before. Much of this data can be dissimilar. IoT enables stream analytics — capturing a stream of data simultaneously from multiple data points and aggregating for analysis in real-time.

Machine learning: a step further
Big data analytics also enables machine learning, allowing organizations to diagnose the potential for a machine malfunction based on analysis of the data it generates. For example, a sensor might detect that a punch in a numerically controlled machine is likely to breach tolerance levels. Stream analytics would then look for data points from other sensors that could suggest the cause of the out-of-tolerance condition. Such detailed, real-time analytics can enable true pre-emptive/predictive maintenance, empowering equipment operators and service organizations to take action long before a problem occurs.


These smart, connected assets can allow enterprises to move beyond real-time control to predictive control, and ultimately even autonomous operation.

Here are three examples from the utilities, manufacturing and transport industries of how IoT is empowering service providers to boost performance and results through predictive maintenance.

Utilities — avoiding catastrophes
Utility companies can leverage IoT and predictive maintenance to boost asset reliability and minimize reactive service costs. Electric utilities routinely gather information about their electricity generation and distribution network via Supervisory Control and Data Acquisition (SCADA) systems. Analysis of data gathered by these systems can proactively alert utility maintenance crews of a pending problem in a power substation, an individual transformer, or other parts of the distribution network. Natural gas utilities can leverage smart sensors to detect potential problems in compressors, corrosion and leaks in gas pipelines, or other pipeline components.

Data gathered by these remote sensors can be shared in real-time with pipeline operators.

Oil pipelines share many of the same issues as gas pipelines — information about failures or potential failures in the pipeline infrastructure must be immediately conveyed and remedied. IoT and predictive maintenance practices are crucial in helping utility companies avoid electricity outages, dangerous gas leaks, oil pipeline breaches, environmental catastrophes, and more.

Manufacturing — good vibrations
Manufacturing equipment often contains multiple mechanical components which must be aligned and calibrated properly. Such equipment have tolerances it must operate within to produce a high-quality end product. Sensors embedded in the manufacturing equipment can monitor the level of vibrations within the equipment and detect if vibration levels have, or are about to, extend beyond specified constraints.

Some manufacturing equipment must operate within certain temperature parameters as well. Sensors can monitor and track those temperature levels and immediately determine if operating temperatures are close to or outside of allowable parameters. These intelligent devices can automatically and immediately alert operators about a new or impending service requirement and potential failure. Armed with that information in advance, the organization charged with maintaining the equipment can provide predictive service, based on the condition of the equipment and, rather than a static time schedule. Such predictive service circumvents future equipment downtime and the associated costs.

Transport — opening doors
IoT data enables fleet, long-haul trucking, railroad, and other transportation operators to anticipate vehicle service requirements. The data allows operators to proactively maintain those vehicles based on the equipment’s specific needs, rather than a number of miles or time interval since the last maintenance. A sensor-equipped vehicle exhibiting consistently high engine temperatures can be brought in for inspection and remedial maintenance, avoiding a costly breakdown. A tire pressure sensor on a delivery vehicle can automatically alert fleet maintenance staff that a tire may need to be repaired or replaced, thus sidestepping a potential delay in deliveries — or worse, an accident.

A problem shared is a problem solved
In addition to monitoring the performance of individual pieces of equipment, smart devices and IoT can also be used to collect data from multiple pieces of equipment of the same type, creating large amounts of information that can be aggregated, analyzed and modelled. This accumulation of performance and reliability data enables comparisons of individual pieces of equipment with others of the same type or model. Comparative data can help service providers identify individual units that are operating outside the norm so corrective action can be taken proactively.

The accumulation of this data allows engineering, manufacturing, and service providers to identify product quality issues so they can be corrected in future versions of the product, or corrected for existing equipment through the creation of field upgrades and engineering changes. Underpinning this predictive maintenance strategy needs to be software that facilitates the analytics and modelling of all the data gathered from smart devices and IoT. The new generation of ERP software solutions are designed with agility and future technology in mind, making it possible for organizations to experience predictive maintenance and its efficiency – ultimately helping to harvest the benefit at the bottom line.

Smart devices, smart solution, smart profit
The enterprise software supporting these smart devices and IoT make it possible for organizations to visualize and analyze equipment performance data in ways not previously possible.

Customers enjoy improved equipment performance, reliability, fewer outages, and longer equipment life. Service providers can implement predictive strategies for more efficient and cost-effective operations. They can plan optimized maintenance schedules in advance and significantly reduce reactive break/fix service events. Service technicians are more productive and can become the customer’s trusted advisor. Engineering and manufacturing can improve future product quality. At the top level, management will see greater operational productivity, reduced service costs, and stronger financial performance.

It’s a win-win approach for service and asset-heavy organizations, with the benefits of this evolving maintenance strategy extending right across the enterprise.

Patrick Zirnhelt is the vice-president of Enterprise Service and Asset Management at IFS North America.

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