Senseye integrates seamlessly with your existing infrastructure investments, using data captured by your factory historian or IoT middleware to understand the future health of your machinery. The process is entirely automated, so there is no need for extensive condition monitoring or knowledge to get it up and running. By collecting key data such as abnormal vibrations, current or pressure and temperature fluctuations, the system generates machine behaviour models automatically, which can then be used in your on-going predictive maintenance efforts.
The history of predictive maintenance, or perhaps more specifically the crucial analysis of machine data that makes predictive maintenance possible, has long been dominated by laborious and expensive processes. Typically, these would involve running a machine to failure, monitoring its components with sensors and recording the data produced during that period. The data would then be analyzed by experts for patterns of behavior and anomalies that would indicate wear, tear and signs of potential failure. Even once this exhaustive preparatory work is completed, ongoing monitoring of machines and analysis of the data they produce still requires some level of manual intervention and interpretation from people with advanced analytical skills. Instead of working to a predefined maintenance schedule or responding once a failure had occurred, aircraft engineers were able to provide the appropriate maintenance at precisely the right time.
Forget sci-fi visions of a future ruled by artificially intelligent computers -- AI is already doing extraordinary work across fields like medicine, the arts, commerce and more. Machine learning, in particular, is proving its potential as companies around the world make use of the technology that powers a computer's ability to "learn" based on real-world data inputs rather than explicit programming. As the machine learning industry grows, Austin continues to be on the cutting edge of this futuristic technology. We've rounded up some of the most exciting companies in the machine learning world; learn their names now so you can say you knew them when. CognitiveScale proves that no company is too large nor too small to reap the benefits of machine learning.
Brexit has been delayed again and the United Kingdom is now set to remain a member of the European Union until 31 October – but with the option to leave earlier if Prime Minister Theresa May can finally secure support for her withdrawal deal. In the short-term, the'Brextension' prevents the UK from defaulting to leaving the EU with no deal on Friday 12 April, avoiding the damaging prospect of the UK leaving without a transition period to decide on its future relationship with the EU. It's also the second-time Brexit has been delayed, as the UK was initially supposed to depart on 29 March – exactly two years the government invoked Article 50, stating the intention to leave. In the months since, organisations in the UK technology industry have been attempting to prepare for a situation which, for many, remains unclear. While some are thankful for more time to prepare for various different Brexit scenarios, others remain confused about what's going on and what the potential of a deal, no deal – or even No Brexit – might mean for business going forward.
The focus of this Predictive Maintenance report is on industrial equipment and machinery but the lessons learned can be leveraged by any organization looking to implement PdM. The Predictive Maintenance report may be most beneficial to OEMs looking into PdM as a new business model, technology vendors in the IoT & PdM space. Also relevant for companies looking into M&A targets in the PdM area, as many Startups mentioned. IoT Analytics is the leading provider of market insights & competitive intelligence for the Internet of Things (IoT), M2M, and Industry 4.0. The specialized data-driven research firm helps more than 40,000 Internet of Things decision-makers understand IoT markets every month. IoT Analytics tracks important data around the IoT ecosystem such as M&A activity, Startup funding, company projects, use cases and latest developments. Product offerings include in-depth market reports, technical whitepapers, sponsored research, regular newsletter, as well as Go2Market and consulting services. As a research pioneer, IoT Analytics combines traditional methods of market research such as interviews and surveys with state-of-the art web-mining tools to generate high-caliber insights. IoT Analytics is headquartered in Hamburg, Germany.