Goliat is the first floating production field development in the harsh environments of the Barents Sea. The customer requires to further enhance oil spill detection and combating capabilities at Eni Norge’s Goliat field.

WIN-911’s partner product ALERT is a key element in the Supervisory Control and Data Acquisition (SCADA) system “FabView” from Air Liquide. Air Liquide is the second largest supplier of industrial gasses in the world. This SCADA alarm notification system is specifically designed to monitor, among other things, the distribution of gas in a semiconductor manufacturing plant located in Dresden Germany. Like most operations of its type, this facility can’t afford much downtime in its distribution facility. If ALERT detects a leak in a pipeline, the system evaluates how severe the leak is and transfers the data back to the central site or home office. Once the information is back in the main office, the SCADA alert system then transcribes the data into understandable messages to which employees can respond appropriately.

If a truck at an active site has a pump failure, Baker Hughes must immediately replace the truck to ensure continuous operation. Sending spare trucks to each site costs the company tens of millions of dollars in revenue that those trucks could generate if they were in active use at another site. The inability to accurately predict when valves and pumps will require maintenance underpins other costs. Too-frequent maintenance wastes effort and results in parts being replaced when they are still usable, while too-infrequent maintenance risks damaging pumps beyond repair.

Data management system versiondog, an indispensable instrument in operations engineering at Warsteiner. Preventive maintenance has long been the norm at the Warsteiner Brewery. And yet, unplanned maintenance activity can still occur. It is therefore of great importance that the latest update version for each and every PLC be stored centrally. In this industry, there is no time to waste trying to conduct a search.

During critical junction time, multiple decision must be made without hesitation and fear. Companies are required to make quick repairs to pieces of gear. Solution is needed to complete challenges during those times.

While mechanical service is still needed, incre- asing digitalization of cars has led to the need for rmware and software updates to complement service and repairs. Instead of repairing cars once they have broken down, real-time data from connected cars enables predictive and proactive service. This creates new opportunities for OEMs to increase the direct relationship to the end customer. Conversely, repair shops need to adapt to these new conditions and innovate new business models to ensure future competitiveness.

Ensure lights are effectively utilized by advertising billboards to increase visibility, revenue and reduce utility costs, while offering multiple connectivity options to customers globally.

Companies are facing greater challenges than ever before when it comes to making sure their cooling equipment operates at maximum efficiency. One of the challenges is the need to understand how equipment is performing to optimize their use, their health and to anticipate issues and failures. Having an HVAC service engineer who continuously monitors the chiller is unrealistic.

Having no unplanned outages in multi-unit power plants throughout the late spring, the summer and the early fall months is challenging in the hot Texas weather. An unplanned outage during these months could mean having to purchase replacement power at spot market prices, which could be spiking during the outage. Knowing when and for how long to overload the equipment in power plants is a significant part of operating strategy. Operators need accurate estimates of motor driven pump and fan loading levels to determine operating limits.

The client is looking to generate higher value from the elevator data that is collected. Sensors and data include:Laser - position of the elevator carLuminosity - Level of light within the carUltrasound - Open shaft doorVibration - Acceleration of the car; vibration of the carMicrophones - abnormal sounds of the carAtmospheric Pressure - Air pressureHumidity - Shaft humidityTemperature - Shaft temperature 

Issues involved in expanding commercialization of wind powerIn 2015, the worldwide capacity of renewable energy facilities exceeded that of coal-fired power.*1 With the aim of creating a low-carbon society, in July 2012, Japan put into effect the feed-in tariff scheme for renewable energy, stimulating the construction of solar and wind farms. In 2016, the full liberalization of the electrical retail business resulted in an increasing number of companies planning either to enter the power generation field or to expand their business. These market conditions engendered a need for the development, design, manufacturing, and sales of wind turbines optimized for Japan's environmental conditions. Utilities considering entering the field of wind power also sought assistance in the streamlining of maintenance and other such business operations.

A stripper well or marginal well is an oil or gas well that is nearing the end of its economically useful life. In the U.S., oil wells are generally classified as stripper wells when they produce 10 to 15 barrels per day or less for any 12-month period. These wells account for approximately 18% of the U.S. production. The key ask was to design a solution that would connect these wells often located in remote locations and collect information about their performance and operating conditions. 

To increase efficiency, develop new services, and spread a digital culture across the organization, Enel is executing an enterprise-wide digitalization strategy. Central to achieving the Fortune 100 company’s goals is the large-scale deployment of the C3 AI Suite and applications. Enel operates the world’s largest enterprise IoT system with 20 million smart meters across Italy and Spain.

By 2025, Transport for London will have to meet strict emission-control regulations. This means buying and operating new fleets of hybrid or fully electric, zero-emission buses. As a consequence, many Original Equipment Manufacturers (OEMs) and operators will have to develop new technologies to help them get-to-market fast enough to meet demand.

Sodexo, the leader in facilities management, is helping manage the operations of the North American headquarters of one of the world’s largest CPG companies. Both the client and Sodexo were looking to transform their respective workplace and modernize workflows. Sodexo was accustomed to practicing route-based maintenance but the lack of insights in the machines they managed hindered them from better understanding the true health of those machines. It wasn’t just about cost saving, it was about leveraging technology to transform how they worked. Sodexo needed a partner that could digitally transform their program, provide continuous insights into the health of their machines, all while bringing them into the new world of Industrial IoT. How could the team optimize asset performance? How could they increase productivity while extending the life of equipment? How could they leverage decades of hands-on experience with technology to accomplish more? 

Waterford Township has been faced with losing a significant number of DPW staff, some with more than three decades of water and wastewater knowledge, to retirement. The company began to search for a solution that would like real-time operational data from its SCADA systems to its CMMS and DMS to create standard operating procedures and work orders automatically when conditions were met in defined workflow procedures. One key aspect of the project was to get operating procedures standardized and in a format where staff in the field, who might not be familiar with the system, could follow the necessary steps to correct the issue.

To develop a reliable and integrated asset management platform:The objective of the platform was to support condition-based monitoring in order to keep in check the asset’s health, predict failure or breakdowns and ensure proactive maintenance decision-making on the basis of the historic data.

Failure prediction by human operators requires advanced skills, and the limited number of experts cannot monitor all MRI systems around the world. "Corrective maintenance" for repairs after breakdowns has also become inevitable.

The Condensate Cooling Water (CCW) pump, one of the critical pumps in maintaining steadystate operations, is a horizontal vane pump operating at up to 1650 m3/hr with a discharge pressure of 9 MPa (62 psi) at 986 rpm. Each day this pump is offline costs the plant $250,000 in lost revenue and each failure costs tens of thousands of dollars to execute an unplanned repair. Thus, Larsen & Toubro (L&T) really needed a predictive maintenance solution to detect faults at an early stage and provide a reliable prediction of Remaining Useful Life (RUL)

To improve production capacity and avoid downtime, a global biotechnology manufacturing company implemented Seebo Predictive Analytics.The company’s quarterly operations review revealed a 3.6% increase in downtime during production. This downtime stemmed from an unexplained viscosity in one product in the production line.The resulting pipeline blockages between the reactor and the centrifuge in the production line led to more frequent equipment cleaning procedures and stoppage during the batch production, high levels of waste, a decreased capacity, and lengthened time to market.The investigative team could not identify a reason for the blockage, as all relevant production parameters were in the approved working range.

Seeing that one of their goals is to find ways to better answer consumer and business needs, the Global Engineering (GE) team was facing the challenge of improving processes and performance of the surfacing machines to significantly improve their production by using the increasing volume of data."We wanted a data science platform that would allow us to solve our business use cases very quickly. Thanks to Dataiku and its collaborative platform, which is agile and flexible, data science has become the norm and is now used more widely within our organization and around the world," said Cédric Sileo, Data Science Leader at Global Engineering, Essilor.

The process condensate pump, one of the critical pumps in the manufacturing process, has a history of failures every 6 to 12 months. It is a centrifugal pump operating at 3000 rpm with a discharge pressure of 28 MPa (400 psi). Each day this pump is offline, it costs the plant as much as $145,000 in lost production and each failure costs tens of hundreds of dollars to execute an unplanned repair. Nanoprecise Sci Corp was asked to implement a predictive maintenance solution in order to detect faults at an early stage and provide a reliable prediction of Remaining Useful Life (RUL).

SCCC had committed to running a showcase Digital Factory for the ASEAN region and had already invested heavily in smart factory equipment and sensors. They required a predictive maintenance system that would leverage their existing investments and integrate with their SAP PM maintenance system.

Historically, Texmark's operations and maintenance teams monitored equipment manually and through wired sensors at a significant cost of dollars and man-hours each year in inspecting equipment on site.Wanting to act quickly and execute on this multi-year plan, Texmark turned to Deloitte to deliver an end-to-end IIoT implementation in late 2017. Over the course of the next two years, Deloitte coordinated the following partners for the project:1. National Instruments to outfit pumps with sensors that collect operational data2. Hewlett Packard Enterprises to enable deployment at the edge3. OSIsoft PI to aggregate the sensor data from the NI sources4. SparkCognition™ to predict impending pump failures5.Flowserve to help develop the model and data flow architecture 6. PTC ThingWorx to create a mobile-supported interface

Bogies are the most significant components of the rail fleet in terms of lifecycle costs and traffic safety. In addition to creating significant cost savings for the rail fleet owners, data-driven maintenance will enhance safety and the usability of the rolling stock. The predictive maintenance capability will improve reliability of the trains, cost-efficiency and passenger comfort. Train traffic will operate more reliably when it is possible to predict rolling stock malfunctions before they cause disruptions in traffic.

Seeking to add value beyond pump efficiency, Armstrong wanted to help customers address the issue of predictive maintenance through continuous learning to improve efficiency and by sharing best practices across industries and buildings.

Reducing production lossesIn 2013, the company identified a need in the maintenance and operation of its acid nitric plant to reduce production losses and improve annual production. Vale noticed there was a gap in nitric acid production from 2011 to 2012 and discovered that three pieces of equipment were responsible for the main losses, including two weak acid condensers and a compressor discharge air cooler. The condenser’s losses were due to thickness loss, lack of availability of the spare condenser, and shell cracking.With a production loss above 14,000 tons in 15 months, Vale aimed to reduce annual loss by 10,000 tons by August 2015. 

Mining companies have a lot of data at their disposal. Sensors are seemingly everywhere in their underground operations. But thus far it has been very hard for mining companies to capitalize on all their data because of the difficulty in making sense of it all.So what’s the most important data for mining companies? The short answer: assets. Mining is one of the most asset-intensive businesses there is. At every point in the extraction chain— drilling, cutting, crushing, screening and removing ore-bearing rock—heavy equipment is critical. And it takes a beating. When equipment breaks down, requiring unscheduled maintenance, production takes a hit, costs rise and a critical measure of capital efficiency in mining—overall equipment effectiveness (OEE)—goes down.

Gerdau was looking to reduce their annual maintenance spend while also improving productivity, thus targeting margin improvements within their manufacturing operations. 

In tandem with its 6 year-long smart meter rollout plan, Con Edison sought to implement Advanced Metering Infrastructure (AMI) operations on top of a comprehensive enterprise data analytics platform for improved operational insight and customer service for its base of more than four million customers. In order to improve customer service and operations across its region, one of the largest integrated utilities in the United States has rolled out the C3 AI Suite and C3 AMI Operations application on AWS. Con Edison’s project objectives were to deliver on the utility’s commitments for presenting customer data, establish AMI operations across 5 million smart meters to ensure operational health, and build a federated data image platform for analytic capabilities. The utility’s smart meter deployment will generate between 100 terabytes and 1 petabyte of data per year, so choosing a platform that could scale and continue to perform analytics on an ever-larger data set was vital.