Improving Predictive Maintenance Time-to-Value with Real-Time Visual Edge Analytics

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1 Improving Predictive Maintenance Time-to-Value with Real-Time Visual Edge Analytics WHEN IT MATTERS, IT RUNS ON WIND RIVER

2 EXECUTIVE SUMMARY Connected computing solutions are deployed worldwide in many different markets, including industrial automation, networking, healthcare, energy generation, transportation, and more. As more and more machines are managed by networked smart devices, the potential of predictive maintenance expands exponentially. Predictive maintenance has the dual benefit of optimizing equipment uptime and performance while reducing the time and labor associated with inspections and preventive maintenance. This paper compares predictive maintenance with the traditional methods used to maintain deployed assets in the Internet of Things (IoT), exposing the latter as a costly, labor-intensive process that cannot ensure failure won t occur between inspections. TABLE OF CONTENTS Executive Summary... 2 Introduction Traditional Approaches to Asset Maintenance... 4 Real-Time, Visual Edge Analytics for Predictive Maintenance... 4 The Predixion and Wind River Solution IoT Security with Wind River Helix Device Cloud Multiple Layers of Protection and Exhaustive Mitigation Strategies... 5 Combined Solution Architecture... 6 Extending Predixion Analytics to the Cloud A Seamlessly Integrated IoT Solution for the Highest Level of Security... 7 Conclusion White Paper

3 INTRODUCTION Connected devices and machines are an integral part of operations in just about every industry today. Whether in a factory, mine, fleet, hospital, or home, these devices are producing a massive amount of data, which is projected to reach 1,600 exabytes by the year 2020 (ABI Research, Competitive Edge from Edge Intelligence IoT Analytics Today and 2020, May 1, 2015). Many organizations are scrambling to securely capture and harness the power of IoT information so they can apply real-time operational insights regarding the health of the devices and their environment and get ahead of unplanned downtime. Organizations are looking for faster ways to realize the value of sensor information and transform it into predictive maintenance insights that users can act upon instantly. According to ABI Research, only 10% of today s IoT-generated data is ever put to use, and even less makes it to the cloud for deeper analysis. Unfortunately, the common approach is to amass all this sensor data to the cloud and treat it as a Big Data or data lake problem. But this approach misses how Moore s Law has altered the landscape of devices and gateways. The processing power of devices and gateways at the edge now rivals that of small computers just a few years ago yet the volume and velocity of IoT data require that real-time analytics move closer to the edge, not closer to the cloud. The result of this edge paradox is that informed decisions are never made, because insight comes too late in the data chain to use in making timely decisions that impact business outcomes. Cloud-Based Analytics Advances in edge computing and IoT data analytics have created an opportunity to transform the way maintenance is performed in a variety of environments, across all industries from factories to fleets. By implementing a next-generation edge architecture for real-time advanced analytics at the edge, organizations can realize significant cost savings by reducing unplanned machine downtime and optimizing procurement for parts and service. Edge analytics helps customers make informed decisions sooner rather than later. To eliminate the guesswork and truly evolve into a predictive enterprise, organizations apply modern, edge-based architectures designed to capitalize on streaming machine sensor data to instantly deliver actionable insights in time to predict and avoid any any unplanned maintenance and downtime. To solve the problems posed by the edge paradox, Predixion and Wind River have created a joint IoT edge analytics solution that provides predictive maintenance capabilities by easily connecting customers devices to a secure, scalable platform for real-time, visual, streaming analytics through a high-performance Wind River powered gateway packed with advanced analytic processing power. Predixion s visual edge analytics software, Predixion RIOT One (for gateways), Predixion RIOT Nano (for devices), and Predixion RIOT Enterprise (for the cloud), along with Wind River Helix Device Cloud, a cloud-based platform for connecting and managing IoT devices, together provide the necessary infrastructure to rapidly and securely deliver high-value predictive insights that dramatically reduce maintenance costs and avoid expensive equipment failures and downtime. Edge-Based Analytics GATEWAY GATEWAY GATEWAY Insight Insight Insight USERS USERS Figure 1. Cloud-based vs. edge-based analytics 3 White Paper

4 TRADITIONAL APPROACHES TO ASSET MAINTENANCE Historically, companies have sent field technicians out to perform routine diagnostic inspections and preventive maintenance according to fixed schedules. This is a costly, labor-intensive process with little assurance that failure won t occur between inspections. Despite the traditional preventive measures put in place to avoid downtime, asset maintenance is often rife with unexpected failure, requiring unplanned maintenance. Traditional checklists or quality control procedures provide insight, but only after the devices and assets have begun to show signs of failure, or have even already failed. Preventive maintenance is required by most organizations, but with schedules that have been predetermined by the device manufacturers, these schedules often lead to replacement of parts well before they re required. Unnecessary part replacement and unrequired servicing creates tremendous expenses, especially considering that the maintenance may not be based on any operational data or preventive analysis. When unforeseen device failures occur, organizations typically move to a reactive maintenance model, which is prohibitively expensive and substantially inefficient. Due to a lack of information, service technicians are left scrambling to limit operational losses and bring equipment back online. Predictive maintenance using edge analytics catches the issues early and at the source to identify and address potential machine issues and failures before they happen. It also optimizes the end-to-end service operations and results in an overall improvement in asset reliability and performance. REAL-TIME, VISUAL EDGE ANALYTICS FOR PREDICTIVE MAINTENANCE The value of implementing an IoT strategy for predictive maintenance using Intel gateways, Wind River Helix Device Cloud, and Predixion RIOT for edge analytics is that maintenance becomes real-time, dependable, secure, and truly predictive. By using realtime analytics to perform timely maintenance on assets, an organization can move away from reactive maintenance. Predictive maintenance maximizes the lifespan of a connected device and ensures optimized production using actionable, advanced visual analytics to keep a watchful, predictive eye on operations. In industries such as oil and gas, even one unanticipated oil rig component breakdown could have catastrophic effects on the overall system, and potentially even become dangerous. Additionally, the resulting unplanned downtime could be hours, days, or even weeks, depending on the severity of the problem and the complexity in returning to normal operations. This example illustrates the tremendous value in harnessing the device data as part of an overall IoT strategy, so problems can be seen well before they get to the point of severity. Visual insight and predictive analytics not only help avoid unanticipated failures, but also optimize overall operations while significantly reducing overall resource and repair costs. THE PREDIXION AND WIND RIVER SOLUTION To deliver this data-driven predictive maintenance solution, Intel gateways with Device Cloud provide the infrastructure and device management to securely connect and manage gateways and devices at the edge, at scale. Predixion s RIOT edge analytics solutions provide instant-on actionable insight by delivering realtime advanced visual analytics surfaced in any modern browser. Together, this seamless end-to-end solution provides: Rapid deployment: Organizations can dynamically deploy advanced analytics on-device, on-gateway, or in the cloud using Device Cloud and Predixion RIOT. Deep security: Multiple layers of security protect against everevolving security threats and device intrusion attempts. Continuous actions: Advanced analytics at the edge support both connected and partially connected environments, so decisions can be made in real time or connection-time. Action-oriented visual analytics: Device Cloud and Predixion RIOT Edge Analytics allow organizations to conduct predictive maintenance and predictive operations management, with workflows triggered by the output of the analytics. The secure connection provided by Device Cloud on Intel gateways allows for device management at the edge, including software and model updates for Predixion RIOT edge analytics in real time. With these real-time, actionable insights, customers can make data-driven decisions based on real-time analytics across their entire field deployment, rather than the more subjective methods previously used. 4 White Paper

5 IOT SECURITY WITH WIND RIVER HELIX DEVICE CLOUD While the case for implementing an IoT strategy and transforming to a predictive maintenance model is clear, this approach also requires that organizations inherently support security and protect IoT systems from all threats. Device Cloud delivers highly advanced and effective security, developed to address the fact that piecemeal approaches simply do not work. It is imperative that the IoT strategy start at the design stage and extend throughout the application lifecycle. Most security threats fall into six categories, and an end-to-end mitigation strategy must provide security and protection from all of them, from the most common threat to the most sophisticated. MULTIPLE LAYERS OF PROTECTION AND EXHAUSTIVE MITIGATION STRATEGIES The strategies deployed to secure IoT implementations can be organized by the types of threats organizations are vulnerable to, classifying attacks that come from a variety of sources into a few distinct categories. One insidious and common method of attack is known as social engineering, which refers to the ability of hackers to obtain security credentials from legitimate users through various means, such as phishing or even blackmail. With its security protocol, Wind River designs in fundamental safeguards, including strong passwords, frequent password updates, role-based permission and access, and software updates that instruct users when common vulnerabilities and exposures (CVEs) have been encountered. But these measures are not enough to address attacks, especially those on hosted components, so Wind River also requires API authentication tokens along with role-based access that whitelists applications to protect against impersonation at the cloud level. Hackers trying to gain access to software at the device level in order to disrupt or take control of the system are met with an impenetrable wall, effectively blocking the successful execution of varied hacking techniques, including denial of service, malware installation, false identities, elevation of privilege, jail breaking, and others. Authenticity and integrity is maintained with secure boot, upto-date software, and an ever-growing library of potential CVEs, addressing them with strategy contained in secure package management (SPM), authentic updates, and signature keys. Users are alerted well before new CVEs are widely known, effectively mitigating the risk. Built into the architecture, integrity measurement verifies the authenticity of running code. Compartmentalizing software into separate containers provides isolation so that a potential breach does not reach the rest of the system, effectively corralling and trapping the hacker attempting to gain access through a software component before they can execute an elevation of privilege in an effort to gain access to the system as a whole. Should a device be stolen and replicated or reverse-engineered in an attempt to steal credentials or other sensitive data, several systems guard devices and data. File system encryption ensures that stolen data cannot be read or copied, and trusted platform modules (TPMs) store encryption keys used to authenticate the hardware on which the host system is running. The integrity of all the different parts of an IoT system throughout its lifecycle can be remotely checked through a management console, allowing the devices to query at any time to confirm identities and verify that no tampering has occurred. Real-time machine data analyzed by Predixion RIOT on Device Cloud provides data-, device-, and analytic-level security to protect networked assets that may be vulnerable to network compromises or man-in-the-middle attacks. In these attacks, hackers attempt to hijack a session to enter a network to disrupt, block, or alter communication between devices and Device Cloud a type of attack of particular concern to an IoT system. By deploying highly varied sets of encryption methods to protect data in transition by streaming data and rendering it unreadable to hackers in the event of a breach, Device Cloud addresses these types of security threats. Lastly, and sometimes at the hand of a well-meaning developer, security misconfiguration can leave an IoT application vulnerable to system-wide attacks intended to find and exploit weaknesses. This threat can also be the result of home-grown or incomplete solutions that are not intended to be a component in an overall IoT strategy or solution. When standalone solutions are knitted together to build an IoT strategy that is ad hoc, they can leave several blind spots and lack executive oversight often providing a false sense of security. 5 White Paper

6 COMBINED SOLUTION ARCHITECTURE Devices will connect to the gateway or edge device running Wind River Linux, Device Cloud, and Predixion RIOT, and begin to stream IoT data through the device and gateway in real time. Prior to Predixion RIOT edge analytics, IoT data would largely go to waste, as it would be transported to another location and stored for later processing, missing the opportunity to act in real time. Predixion RIOT takes advantage of Intel s edge device location and relationship to the connected machines by processing IoT data at the edge and instantly delivering visual insights. This capability limits the need for transporting and storing the high volume of IoT data and allows analytical insights to be delivered while they are still relevant. Once devices have passed the Device Cloud security protocol and established a connection with the gateway, live IoT data can then begin streaming to the preinstalled Predixion RIOT. The streaming data is ingested by Predixion RIOT with mainstream protocols such as MQTT or REST. As the secure, real-time data is streaming and passing through the gateways, Predixion RIOT applies advanced analytics, immediately pushing the results to a highspeed, cloud-based sensor hub for enterprise analytic orchestration. This high-speed hub serves as an orchestration mechanism for enterprise-wide, real-time, visual analytics, and is also the mechanism by which Predixion applies advanced and predictive server-based deep analytics. With the sheer volume of data flowing through gateways now, traditional database dump and query methods simply will not suffice. Edge analytics grow in value as more devices connect and more data flows, requiring solutions that are scalable and highly performant end to end. Historical AN INTEL COMPANY Real Time Real -Time Insight & Analytics Predictive Insight REST MQTT AMQP REST MQTT AMQP REST MQTT AMQP ANALYTICS PATTERNS PREDICTIONS ANALYTICS PATTERNS PREDICTIONS ANALYTICS PATTERNS PREDICTIONS DEVICE DEVICE DEVICE DEVICE DEVICE DEVICE DEVICE DEVICE DEVICE Figure 2. Predixion RIOT applies advanced analytics to real-time data 6 White Paper

7 EXTENDING PREDIXION ANALYTICS TO THE CLOUD An advanced IoT strategy with Predixion RIOT Enterprise creates greater value and insight for cross-device, enterprise-wide analytics. This solution includes powerful cloud-based micro-services and reacts to both user and device asynchronous events at scale. A sophisticated analytics-based partitioning architecture between edge and cloud micro-services is orchestrated by a powerful set of analytics engines; the net result is a highly visual and interactive browser-based analytical IoT dashboard that puts deep operational and tactical analysis in the user s hands. IoT analytics therefore moves into the realm of predictive maintenance, creating a highly reactive and actionable predictive maintenance solution. A SEAMLESSLY INTEGRATED IOT SOLUTION FOR THE HIGHEST LEVEL OF SECURITY When developers attempt to build a complete IoT solution using a myriad of IoT products, they commonly hit complexity and security road blocks immediately. Trying to assemble disparate products into an end-to-end IoT solution quickly becomes unwieldy and unmanageable. Solutions are often à la carte and not particularly well articulated or compatible with most customers current technologies. CONCLUSION Companies have historically sent field technicians out on fixed schedules to perform routine diagnostic inspections and preventive maintenance on deployed equipment. But this costly, laborintensive process does little to ensure that failure won t happen between inspections. Predictive maintenance, on the other hand, maximizes the device lifespan and ensures optimized productivity. By harnessing device data, problems can be anticipated well ahead of failure. With the Predixion and Wind River solution for predictive maintenance, companies can rapidly deploy advanced analytics tools, rely on deep security protection from edge to cloud, gain the ability to make decisions in real time, and conduct predictive maintenance and operations management with workflows triggered by the output of the analytics. With these real-time, actionable insights, customers are now able to make predictive maintenance a reality based on real-time analytics across their entire field deployment. Because the IoT market is still new and evolving, many customers are turning to their gateway and device management providers as trusted advisors, looking for expertise that will enable them to execute on a strategy without requiring specialized internal expertise or dedicated teams. Partnered with Predixion, original equipment manufacturers (OEMs) can offer a seamless solution that is easy to deploy, easy to use, and simply works. Embedded Predixion RIOT One is preconfigured to make deployment almost effortless, automating the process of connecting IoT data for immediate results. Having established the baseline for an IoT strategy with a combined, secure architecture, Predixion and Wind River are aligned for advancing an organization to a mature IoT strategy. The baseline solution Intel gateways running Predixion RIOT and Device Cloud supplies the necessary elements for device managers to do away with assumption- or hunch-based decisions, and act on insights delivered with analytics at the edge. Wind River is a global leader in delivering software for the Internet of Things. The company s technology is found in more than 2 billion devices, backed by world-class professional services and customer support. Wind River delivers the software and expertise that enable the innovation and deployment of safe, secure, and reliable intelligent systems Wind River Systems, Inc. The Wind River logo is a trademark of Wind River Systems,Inc., and Wind River and VxWorks are registered trademarks of Wind River Systems, Inc. Rev. 05/2016