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Evaluation of systems (types and categories) and management through CMMS/EAM

Evaluation of systems (types and categories) and management through CMMS/EAM

Introducing issues related to RCM application and management considerations in CMMS/EAM!

This document introduces the evaluation of systems (systems and subsystems) and management through CMMS/EAM. It covers issues encountered when applying Reliability Centered Maintenance (RCM), considerations for implementing RCM, reliability information, evaluation of reliability and availability at the system level, formulation of maintenance strategies, and management points in CMMS/EAM. [Contents] ■ Reliability Centered Maintenance ■ Issues when applying RCM ■ Considerations for implementing RCM and reliability information ■ Evaluation of reliability and availability at the system level and formulation of maintenance strategies ■ Management points in CMMS/EAM ■ Conclusion *For more details, please refer to the PDF document or feel free to contact us.

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Predictive Maintenance AVEVA Predictive Analytics

Predictive Maintenance AVEVA Predictive Analytics

Over 15 years worldwide, with more than 20,000 achievements! Many customers around the world are using it.

"AVEVA Predictive Analytics" is a package filled with a wealth of know-how from past predictive maintenance experiences, allowing users to easily build predictive models. It is also possible to provide a service that monitors your valuable equipment 24/7, 365 days a year at the central monitoring center in Chicago, USA. Many customers around the world are utilizing it. 【Key Features】 ■ Numerous predictive model templates ■ Advanced calculation module definitions ■ Intuitive screening of training data ■ Performance management via web browser ■ Root cause analysis of anomalies ■ Non-stationary analysis ■ Case study management *For more details, please refer to the PDF materials or feel free to contact us.

  • Other Management Services

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[PI System Implementation Case] Juwi

[PI System Implementation Case] Juwi

Improving asset monitoring through data integration! A case study that saved $2.6 million.

Juwi is a renewable energy company that takes on the design and management from project initiation to implementation, as well as providing asset monitoring services after project execution. As the company's asset portfolio expanded, the integration and management became more complex. The number of individual software applications needed for asset management increased, making the integration of new assets more difficult. Therefore, we decided to implement our PI System as an integrated monitoring solution to simplify operations. [Case Study] ■Implementation Site: Juwi ■Implemented Product: PI System *For more details, please refer to the PDF document or feel free to contact us.

  • Company：AVEVA
  • Price：Other
  • Data Centres

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[Information] Comprehensive DX promoted by Shell

[Information] Comprehensive DX promoted by Shell

Infiltrating major energy companies and accelerating digital transformation!

This document presents a case where the energy company "Shell" transitioned from reactive to predictive operations, supporting engineers with data-driven insights and early determination that data is essential for achieving goals. The company, which extracts business value from real-time technology, applies existing enterprise contracts across many operational areas. These contracts are permeating a data-driven culture within the organization and accelerating digital transformation. [Contents (partial)] ■ Introduction ■ Company-wide intelligence through smart solutions ■ Advanced operations utilizing analytics ■ Saving millions of dollars through the digitalization of blowout preventers ■ Supporting greenhouse gas recovery with advanced analytics *For more details, please download the PDF or feel free to contact us.

  • Company：AVEVA
  • Price：Other
  • Database

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AVEVA Predictive Analytics

AVEVA Predictive Analytics

Automatically adapts to changes in input volume, environment, and operating conditions! Can provide warnings before equipment failures occur.

We would like to introduce "AVEVA Predictive Analytics," which we handle. With native integration with the "AVEVA PI System," it is possible to deploy predictive/prescriptive maintenance activities among stakeholders without the need for data scientists or software development. Moreover, the success of predictive analytics solutions depends not only on algorithmic methods but also on expansion methods across the entire business. This no-code dedicated solution enables the deployment and maintenance of predictive monitoring programs throughout the organization. 【Features (partial)】 ■ Native integration with AVEVA PI System ■ Intuitive model building ■ Alerts and notifications ■ Fault diagnosis ■ Time-to-failure prediction *For more details, please download the PDF or feel free to contact us.

  • Company：AVEVA
  • Price：Other
  • Other Software

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AVEVA Advanced Analytics

AVEVA Advanced Analytics

Improve quality, energy efficiency, and throughput with cloud-based normative insights!

Introducing "AVEVA Advanced Analytics," which we handle. Maximize the value of industrial data and improve overall operational efficiency through real-time predictive insights during the production process. By modeling throughput, quality, and energy, you can deepen your knowledge of processes and assets, identify and rectify inefficiencies and issues, eliminate waste, and enhance productivity. 【Features】 ■ No programming skills required ■ Collect and aggregate data from multiple data sources ■ Leverage the power of the cloud to share data with internal and external stakeholders ■ Easily collaborate with data scientists and subject matter experts ■ Utilize dashboards, analytical tools, and analytical reports *For more details, please download the PDF or feel free to contact us.

  • Company：AVEVA
  • Price：Other
  • Other Software

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[PI System Implementation Case] Deschutes Brewery

[PI System Implementation Case] Deschutes Brewery

Enhancing the use of sensor data to improve beer quality! Maximizing production volume through improved temperature control.

In 2015, at Deschutes Brewery, the seventh largest craft beer brewery in the United States, a new type of fermentation tank used in the cooling stage of the brewing process experienced a rapid temperature rise. Initially, equipment upgrades were considered, but first, they attempted to use operational data collected with the 'PI System' to simultaneously achieve a reduction in cooling time and maintain beer quality. Additionally, they added a fourth temperature sensor to the conical bottom of the fermentation tank, which had not been monitored before, and utilized this to implement cooling control for the conical bottom. This led to new discoveries and improvements in control, preventing unnecessary temperature rises. Furthermore, by transitioning to condition-based maintenance (CBM), they were able to avoid $8 million in excessive capital investment. [Case Overview] ■ Industry: Food & Beverage ■ Business Value - Process optimization, increased production, improved quality, democratization of data Predictive analytics, real-time KPI dashboard *For more details, please refer to the PDF document or feel free to contact us.

  • Company：AVEVA
  • Price：Other
  • Database

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[PI System Implementation Case] Flowserve Corporation

[PI System Implementation Case] Flowserve Corporation

Introducing a case study on the use of the PI System to improve equipment reliability and performance: Avoiding a loss of $650,000 due to unexpected operational downtime.

Flowserve Corporation, headquartered in the United States, is a global company specializing in fluid control equipment, including pumps and mechanical seals. In this instance, we will introduce a case where Flowserve's global rotating equipment monitoring solution was realized through OSIsoft's connected services. Currently, manufacturers around the world are embarking on strategies to integrate digital intelligence into industrial equipment to improve uptime, prevent accidents, and enhance the bottom line. The amount lost by the process industry due to unexpected downtime and inadequate maintenance reaches $20 billion worldwide. Approximately 80% of these losses could be prevented if information were accessible at an early stage. [Case Overview] ■ Industry: Manufacturing ■ Business Value IoT, remote performance monitoring, collaborative data ecosystem Aftermarket (maintenance) service provider for industrial applications Connected services, OEM *For more details, please refer to the PDF document or feel free to contact us.

  • Company：AVEVA
  • Price：Other
  • Database

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[PI System Implementation Case] Sandvik Coromant Company

[PI System Implementation Case] Sandvik Coromant Company

A battle against time! The trajectory from PoC of Industry 4.0 to "value realization on a company-wide scale."

In 2015, Sandvik Coromant embarked on a pilot project for a Proof of Concept (PoC) to track, manage, and optimize equipment performance using our "PI System." While the project team requested a 10-week grace period to demonstrate value, management demanded proof in just 4 weeks. As a result, the project team was able to quickly model each system using the Asset Framework of the PI System, ingest data from a wide range of diverse sources, analyze it, and provide rapid feedback on the results. These tools from the PI System were a tremendous help for the time-constrained project and led to a successful demonstration. [Overview] ■ Client: Sandvik Coromant ■ Industry: Manufacturing - Industrial Machinery Manufacturing ■ Product Implemented: PI System *For more details, please refer to the PDF document or feel free to contact us.

  • Company：AVEVA
  • Price：Other
  • Database

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[PI System Implementation Case] Sweden Hydroelectric Power Plant CBM

[PI System Implementation Case] Sweden Hydroelectric Power Plant CBM

Covers about 90% of all failure modes without additional sensors! Discovering new efficiencies through condition-based maintenance.

Vattenfall Hydro, a Swedish company, is a major hydropower operator in Europe. The management team realized that there was a need to change the company's asset maintenance efforts. For the previous 15 to 20 years, they had been using condition-based monitoring, but relied on an old system that depended on periodic inspections, tests, and static data from data historians, resulting in many reactive maintenance practices. To improve efficiency and reduce operational costs, it was necessary to transition to real-time condition-based monitoring using measurement instruments. [Case Study] - Implementation Site: Vattenfall Hydro Power - Industry: Power Generation, Hydropower - Implemented Product: PI System *For more details, please refer to the PDF document or feel free to contact us.*

  • Company：AVEVA
  • Price：Other
  • Data Centres

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[PI System Case Study] Predictive Maintenance at Ansaldo Energia, Italy

[PI System Case Study] Predictive Maintenance at Ansaldo Energia, Italy

You can perform a comparison between actual data and estimated data! Real-time notifications enable prompt responses, achieving predictive maintenance!

Ansaldo Energia is a company based in Genoa, Italy, specializing in the construction of thermal power plants, primarily engaged in plant engineering, construction, and services. It owns over 100 power plants and more than 300 gas turbines worldwide, providing remote monitoring services to customers in 13 countries. The PDF document introduces how the company utilizes the PI System for remote asset monitoring and predictive maintenance. [Case Study] - Implementation Site: Ansaldo Energia - Industry: Power Generation, Construction - Implemented Product: PI System *For more details, please refer to the PDF document or feel free to contact us.*

  • Company：AVEVA
  • Price：Other
  • Data Centres

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Protecting building automation systems from cyber attacks.

Protecting building automation systems from cyber attacks.

Challenges of Cybersecurity in Building Automation Systems (BAS): How to Achieve a Balance Between Data Protection and Operational Efficiency?

Current Building Automation Systems (BAS) face cybersecurity challenges that require them to collect, utilize, and provide critical data while also protecting that data. This issue has two facets: ◇ The data obtained from commercial and corporate building facilities has become more abundant than ever, and there is a growing recognition of the value of this data. ◇ BAS data and its functionalities are exposed to risks of misuse and theft, making them more vulnerable than ever. However, there are several ways to utilize and protect data simultaneously. This document explains the cybersecurity risks that facility management personnel should be aware of. It discusses how these risks have increased in recent years and the potential impacts on users and owners of facilities if data is hacked. Additionally, cybersecurity experts will provide insights on how to mitigate these risks. It will also clarify what should be required of employees and partners to ensure that BAS and its data are securely protected from hackers, disruptors, and other malicious actors.

  • Company：AVEVA
  • Price：Other
  • IT Control and Services

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Four Ways to Derive More Value from Data

Four Ways to Derive More Value from Data

Get more from existing data! We will also introduce root cause analysis and predictive maintenance.

This document introduces four ways to derive more value from the data of the "AVEVA PI System." It showcases how companies in the chemical, oil and gas, mining, and power industries are utilizing data in their initiatives. Additionally, it includes case studies such as "Root Cause Analysis at Covestro" for the chemical industry and "Predictive Maintenance at PETRONAS" for the oil and gas industry. We encourage you to download and read it. [Contents] ■ Gaining more from existing data ■ Root cause analysis at Covestro ■ Predictive maintenance at PETRONAS ■ Automated delay calculations at Rio Tinto ■ Data sharing at Silicon Valley Clean Energy *For more details, please download the PDF or feel free to contact us.

  • Company：AVEVA
  • Price：Other
  • Other Software

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