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Ursalink Provides Stable and Secure Internet Access for Video Surveillance in Se
Ursalink Provides Stable and Secure Internet Access for Video Surveillance in Se
Access to the Internet with wired connection24/7 monitoring and real-time data transmission from equipment
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Largest Production Deployment of AI and IoT Applications
Largest Production Deployment of AI and IoT Applications
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.
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Scalable Predictive Maintenance in Nissan
Scalable Predictive Maintenance in Nissan
With an abundance of data and insufficient skilled resources to perform analysis, Nissan were keen to expand the benefits of using data to influence maintenance. It decided to embark on a Condition Based maintenance programme to reduce production downtime by up to 50% across thousands of diverse assets. It was attracted to Senseye by its strong prognostics offering underpinned by machine learning.
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IIoT Enablement In The Elevator Service Industry
IIoT Enablement In The Elevator Service Industry
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 
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A Hybrid Switchgear-Communication Solution Satisfies Shopping Center’s No-Antenn
A Hybrid Switchgear-Communication Solution Satisfies Shopping Center’s No-Antenn
Based in Bolton, England, Ascribe is a leading provider of business intelligence (BI) and clinically focused IT solutions and services for the healthcare industry. Ascribe estimates that 82 percent of National Health Service (NHS) trusts in the United Kingdom use its products. With access to large volumes of data maintained by the trusts, the company wanted a BI solution that would help healthcare providers detect, predict, and respond more quickly to outbreaks of infectious disease and other health threats. Healthcare analysts typically work from data collected and coded when patients receive treatment in clinics and hospitals. “By the time they get that information it’s usually out-of-date,” says Paul Henderson, Business Intelligence Division Head at Ascribe. “The data has already been coded and stored in a record-keeping system, or it’s been collected from a hospital workflow, and that doesn’t always happen in real time.” In addition, huge volumes of potentially useful data existed in text files from sources such as unscheduled visits to emergency rooms, school attendance logs, and retail drug sales. The Internet offered another trove of untapped information including clickstream analysis and social media such as Twitter. “If you think about each clinician who struggles with getting timely, accurate data, and you compound it on a national scale, then it becomes an immense challenge,” says Henderson. “You have lots of small pieces of data coming in from multiple places, and it can be very difficult to aggregate and interpret.”Ascribe had previously worked on a solution to support the analysis of national emergency care attendance. The system was designed to monitor the daily number of people who visited emergency departments in the UK and raise an alarm when it identified unusual levels of activity such as a potential outbreak of an infectious disease. However, it was difficult to collect data from a rapidly growing number of healthcare providers, including mobile clinicians. In addition, clinicians were unable to use the exploding volume of unstructured data from patient case notes and social media feeds. “The processing power you would need to handle all of that information is beyond the capability of most organizations,” says Henderson. “A hospital can’t just stand up a server farm to process millions of case notes from an emergency care system in addition to other data.” To solve these problems, Ascribe decided to design a proof of concept that would create a standardized approach to working with healthcare data. The company asked Leeds Teaching Hospitals, one of the biggest NHS trusts in the UK, to participate in the project. Leeds can generate up to half a million structured records each year in its Emergency Department system. The hospital also generates approximately 1 million unstructured case files each month.Ascribe wanted to create not just a proof-of-concept BI solution for monitoring infectious disease on the national level, but also a tool that could be used to improve operations for local care providers. “Our goal was to find a way to make data flow more quickly in near-real time,” says Henderson. “We also wanted to augment clinically coded data with data harvested from case notes.” The company wanted to create a national knowledge base that both analysts following disease outbreaks and local clinicians could use to improve healthcare. Ascribe needed a highly scalable, end-to-end solution that could work with multiple data types and sources, as well as provide self-service BI tools for users.
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Università degli Studi di Udine
Università degli Studi di Udine
University of Udine is a college committed to the highest education standards, research, interaction with surrounding territories. The collaboration with Endian brought its technological vocation beyond the academic field to translate into a project aimed to protect and safely manage accesses to electrical and thermal control systems, access control and video surveillance.
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Predictive Maintenance case-studies from Minerals Industry
Predictive Maintenance case-studies from Minerals Industry
SAP
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.
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Predict to prevent: Transforming mining with machine learning
Predict to prevent: Transforming mining with machine learning
IBM
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.
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Ground-breaking Service for Millions Uses Cloud for Online Psychotherapy
Ground-breaking Service for Millions Uses Cloud for Online Psychotherapy
According to the World Health Organization (WHO), there are approximately 1.7 billion people in the world suffering from a range of mental health conditions. This might be anything from acute anxiety and clinical depression to obsessive-compulsive disorders. The WHO believes there are 23 million people suffering from these and similar conditions in Egypt alone. However, one of the factors preventing people from receiving treatments are cultural values and social stigma: they say that either these conditions do not exist, or that if people are indeed suffering, they just need the willpower to overcome their afflictions rather than seeking specialized treatment.With an estimated 10,000 website hits per month, Shezlong needed a hosting platform that could not only provide a foundation for the service but also accommodate its open source architecture.
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Rethinking Machine Performance
Rethinking Machine Performance
Machine uptime is one of the most vital performance factors for electric rotating machinery. In times when an hour of downtime can equate to thousands of dollars in losses, securing predictability turns into a high priority for all industrial businesses. Manufacturers operate in an extremely volatile environment, thus avoiding unplanned downtime becomes critical for achieving desired business outcomes. Predictive maintenance is no longer a nice-to-have but a necessity to survive and thrive in unfavourable conditions. Start from the basics. Making machines perform better means, first and foremost, understanding how the machine works, extracting relevant data, and gaining meaningful insights into ongoing processes. The power of the machine lies in utilizing its full potential.
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Enterprise Data Analytics Platform and AMI Operations
Enterprise Data Analytics Platform and AMI 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.
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Providing a Next-Generation Air Service with SAP® Leonardo Internet of Things
Providing a Next-Generation Air Service with SAP® Leonardo Internet of Things
SAP
To optimize its Sigma Smart AirService, Kaeser worked with SAPDigital Business Services to deploySAP Leonardo IoT capabilities as its innovation foundation together with SAP Asset Intelligence Network and SAP Predictive Maintenance and Service. Kaeser’s new solution connects its compressors smartly in the cloud, allowing it to offer a next-generation service at a lower price.Challenges:- Service team unable to access calibration data and other equipment-specific information, which was stored in on-premise systems- No solution to meet the needs of dealers and companies’ service providers- Need for track-and-trace capabilities with selected suppliers to scale-up potential
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Smart Water Metering - Gavi
Smart Water Metering - Gavi
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Manage HVAC systems to optimize performance and save up to 40 percent
Manage HVAC systems to optimize performance and save up to 40 percent
IBM
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.
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Cooperation with VR FleetCare for predictive analytics
Cooperation with VR FleetCare for predictive analytics
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.
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Tata Power Uses AVEVA PRiSM Predictive Asset Analytics Software
Tata Power Uses AVEVA PRiSM Predictive Asset Analytics Software
- 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
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Aircraft component manufacturer introduces predictive maintenance
Aircraft component manufacturer introduces predictive maintenance
A major European aircraft component supplier encountered this challenge first-hand. A mission-critical, programmable milling machine failed, halting the organization’s production process. Despite the customer team’s expertise, the problem proved challenging to diagnose. At first, it appeared the downtime resulted from a damaged spindle, the most complicated part of the milling machine. However, a costly and time-consuming spindle replacement did not correct the situation. The team was forced to perform an extensive system evaluation to identify the culprit.
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Driving peak performance with comprehensive support coverage from IBM
Driving peak performance with comprehensive support coverage from IBM
IBM
With more than 11,000 employees at 94 locations across India, leading commercial vehicle manufacturer VECV needs seamless, responsive technical support to ensure high-availability IT operations. The company sought services from a trusted IT provider capable of simplifying coverage for its multi-vendor environment, accelerating issue resolution for end-users and structuring an effective governance framework for vendor management.
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Saving millions with a predictive asset monitoring and alert system
Saving millions with a predictive asset monitoring and alert system
IBM
The challenge was to harvest and sift through this data, recognize the patterns that indicate a high likelihood of asset failure, identify the most urgent issues, and get the right information to its engineers with enough lead time for them to take effective action.“Before, we only used between 10 and 12 percent of the operational data we collected, which is the industry average,” comments Benn. “By the time we had searched for, collated and forwarded the right information to the right people, we might respond too late to avoid impact to operations, or have to make last-minute changes to our maintenance schedule, which reduces efficiency. Our challenge was to provide right-time, actionable, effective information proactively, rather than in a reactive or look-back assessment.”“What we wanted was a way to identify patterns in that sensor data that would give us an early warning of asset failure. We saw an opportunity to use analytics technology to extract greater value from the systems and data we already possessed, which would help us to, for example, avoid preventable failures and potentially save millions of dollars.
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Predicting Rare Failures in Hydro Turbines
Predicting Rare Failures in Hydro Turbines
Utility companies that operate hydro turbines have a vested interest in performing regular maintenance to prevent unexpected failures. Most maintenance occurs on a scheduled basis where the asset is taken offline, inspected, and repaired proactively if needed. Hydro turbine units are highly reliable, meaning that few examples of unplanned downtime exist. However, these failures are very costly to their operators.Given the sensitivity operators have to unplanned downtime, many have equipped turbines and generators with sensors and platforms to collect valuable performance information in real-time. But because there are so few historical hydro failures to compare against, rich streaming data and legacy statistics-based analysis are not very accurate at predicting true failure events. In fact, they often create more problems by overloading monitoring teams with benign false positives that result in unnecessary downtime to evaluate. This begs the question: Can artificial intelligence help maintenance teams extract more value out of their data?
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Continuous condition monitoring pays off at a large power utility
Continuous condition monitoring pays off at a large power utility
A large power utility in Hawaii was looking for more frequent condition monitoring on their Balance of Plant (BOP) generation assets. They had experienced significant equipment failures that occurred between their scheduled quarterly walkaround condition monitoring routes.
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How a major player in the oil & gas industry decreased downtime
How a major player in the oil & gas industry decreased downtime
Sean Simon is the SVP of Operations at CIG Logistics, where sand is transloaded and stored for third parties in the oil and gas industry. Before looking into CMMS solutions, his team spent three years trying to manage their maintenance operations with a paper-based system, leaving them with the major issue of not being able to gather or access data. “There’s no way to mine paper. There was no daily summary, no way of tying together comments or keywords.” As a result, trying to track and schedule preventive maintenance was nearly impossible. “It was like owning a car in the 1950s. You had to try to remember the last time you did something and guess at the maintenance that needed to be done in the future”.
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Predictive Analytics Solution for Off Highway Equipment
Predictive Analytics Solution for Off Highway Equipment
The client wanted to reduce downtime and production losses by effectively prioritizing maintenance activities and proactively replacing components before failure.
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Predictive Maintenance Software for Gas and Oil Extraction Equipment
Predictive Maintenance Software for Gas and Oil Extraction Equipment
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.
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Vale Fertilizantes Saves $1.4M in Production Losses with Predix Asset Performanc
Vale Fertilizantes Saves $1.4M in Production Losses with Predix Asset Performanc
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. 
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The Convergence of Predictive and Preventative Maintenance for Mill Reliability
The Convergence of Predictive and Preventative Maintenance for Mill Reliability
Gerdau was looking to reduce their annual maintenance spend while also improving productivity, thus targeting margin improvements within their manufacturing operations. 
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Automotive manufacturer increases productivity for cylinder-head production by 2
Automotive manufacturer increases productivity for cylinder-head production by 2
IBM
Daimler AG was looking for a way to maximize the number of flawlessly produced cylinder-heads at its Stuttgart factory by making targeted process adjustments. The company also wanted to increase productivity and shorten the ramp-up phase of its complex manufacturing process.
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Reducing Downtime with Predictive Analytics
Reducing Downtime with Predictive Analytics
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.
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ArcelorMittal condition monitoring
ArcelorMittal condition monitoring
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.”
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20200226Philtest
20200226Philtest
75F
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