In order to scale up operations, loT Systems’ client needed a system to monitor vacuum pumps and provide managers with actionable data at near real-time speeds from anywhere, at any time, and on any device. The vacuum pumps are part of vertical furnaces that prepare semiconductor wafers at precise temperatures and pressures according to their clients’ exact specifications. If a pump was about to fail and a manager did not see the alarm, it could lead to a scrap event, costing the client revenue, delaying production with unplanned downtime, and increasing overall manufacturing costs. Managers could not see alarms in real time unless they physically walked around the floor, making predictive maintenance and preventing scrap events harder as the client scaled upwards.

One of Europe’s largest integrated electric power companies was looking for analytics solutions to reliably forecast equipment failure and improve condition-based maintenance for its coal-fired power plant. With a diverse array of coal, oil, and gas/CCGT power plants, the utility’s more than 50GW worldwide generating portfolio has been under pressure to streamline global operations and reduce generating costs (both CapEx and operations /maintenance O&M expenses) by 7-10%.

- Avoid asset failures and reduce equipment downtime - Identify subtle changes in system and equipment behavior - Gain advanced warning of emerging equipment issues - Monitor the health and performance of critical assets fleet-wide in real time - Improve maintenance planning y Enable knowledge capture to optimize information sharing between plant personnels

ArcelorMittal’s rotating assets often operate in harsh environments. A conveyor at the company’s hot strip mill in Ghent, Belgium moves plates of sizzling hot steel along the production process. In conditions like these, traditional proximity-based technologies like vibration and acoustic analysis fail: the sensors can’t handle the high temperatures.“In the steel industry, assets frequently operate in conditions that are not hospitable to sensitive sensor technologies,” says Andy Roegis, ArcelorMittal’s industrial digitalization manager for northern Europe. “The conveyor on our hot strip mill is a critical part of the production process, but it’s virtually impossible to use manual or vibration-based techniques to assess its condition.”

Predicting end-of-cycle (EOC) for a heat exchanger due to fouling is a constant challenge faced by refineries. Proactively predicting when a heat exchanger needs to be cleaned enables risk-based maintenance planning and optimization of processing rates, operating costs, and maintenance costs. Before using Seeq, the engineer had to manually combine data entries in a spreadsheet and spend hours/days formatting and filtering the content or removing non-relevant data when necessary (for example when equipment was out-of-service).

Given the extreme volatility in the Oil & Gas market, a global Oil & Gas company was operating as leanly as possible. As such, the company was unable to monitor hundreds of sensors for each turbine on a daily basis, causing equipment maintenance problems to go unnoticed.

Leica Microsystems attributes its success to providing innovative products and superior customer service. To extend its leadership position, the company began exploring a more proactive service approach for its line of confocal microscopes and tissue processors. The Leica Microsystems project team began searching for a global software that would allow for the shift from a reactive to proactive service company. Their initiative focused on downtime avoidance and the prediction of potential problems across the globe, targeting issue prevention. As a result, customers would not only benefit from minimal product downtime, but from faster service and increased productivity. To obtain approval and funding for the initiative, the team would need to prove to management that this service strategy shift would result in optimized instrument uptime and reduced costs of service.

Predictive maintenance refers to techniques that help determine the condition of in-service equipment in order to predict and/or optimize when maintenance should be performed. Predictive maintenance is one of the most important benefits of the Industry 4.0 revolution. 

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.

The extrusion and other machines at Mondi’s plant are large and complex, measuring up to 50 meters long and 15 meters high. Each machine is controlled by up to five programmable logic controllers (PLCs), which log temperature, pressure, velocity, and other performance parameters from the machine’s sensors. Each machine records 300–400 parameter values every minute, generating 7 gigabytes of data daily.Mondi faced several challenges in using this data for predictive maintenance. First, the plant personnel had limited experience with statistical analysis and machine learning. They needed to evaluate a variety of machine learning approaches to identify which produced the most accurate results for their data. They also needed to develop an application that presented the results clearly and immediately to machine operators. Lastly, they needed to package this application for continuous use in a production environment.

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.

An aircraft manufacturer wanted to improve their spare part inventory management for 15,000 out of production aircraft. Customers needed stock parts that couldn’t be sourced since they were never required to be repaired or replaced on the aircraft. The parts would need to be produced on demand and have a long lead time for production - typically up to six months - resulting in airlines grounding their aircraft at an average loss of one million per day. As a result, the manufacturer needed to anticipate which of their spare parts would fail before an issue was communicated - leading to better inventory planning, supply chain decision making, and a total reduction in operating costs. Additionally, the company wanted to know whether to purchase more than one spare part at a given time when a problem had been reported, whether they should stock a certain spare part in advance due to the long lead time, and whether they could reduce the number of service engineer man hours spent answering customer questions about the stock parts by having better access to information. Stocking decision was also made difficult because of limited communication between the spares management employees and the service engineers, resulting in high levels of ‘dead’ stock for the manufacturer. The goal was to reduce maintenance costs and increase customer satisfaction and retention without compromising aircraft production quality, safety and lives.

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.

When manufacturers, such as the world's top car makers and automotive parts suppliers, produce components in their factories, traditional QA testing has been limited to verifying the quality of random parts pulled off the line throughout the day.It was time consuming to perform the detailed tests required, and defective parts could get through despite randomized tests.If a defective part caused a recall or accident, manufacturers could face costly litigation or irreparable damage to their reputation.

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)