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The Wind Turbine Condition Monitoring System market is set to grow by USD 159.5 Million from 2024-2028, driven by AI and increased SCADA adoption in wind turbine systems – Technavio

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NEW YORK, Oct. 23, 2024 /PRNewswire/ — Report on how AI is redefining market landscape – The Global Wind Turbine Condition Monitoring System Market  size is estimated to grow by USD 159.5 million from 2024-2028, according to Technavio. The market is estimated to grow at a CAGR of  8.2%  during the forecast period. Rsing adoption of scada in wind turbine systems is driving market growth, with a trend towards rising necessity of remote and e-monitoring of wind turbines. However, high initial investment and cost in installing wind turbine condition monitoring systems  poses a challenge – Key market players include AB SKF, Advantech Co. Ltd., American Superconductor Corp., Avnet Inc., Bachmann electronic GmbH, Baker Hughes Co., Emerson Electric Co., ENERCON GmbH, Envision Group, Flender GmbH, Fluke Corp., General Electric Co., Hansford Sensors Ltd., HIMA Paul Hildebrandt GmbH, ifm electronic gmbh, James Fisher and Sons Plc, Nordex SE, NSK Ltd., ONYX Insight, Robert Bosch GmbH, Siemens AG, TUV Rheinland AG, and Yokogawa Electric Corp..

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Forecast period

2024-2028

Base Year

2023

Historic Data

2018 – 2022

Segment Covered

Type (Equipment and Software), Application (Onshore wind turbines and Offshore wind turbines), and Geography (APAC, Europe, North America, South America, and Middle East and Africa)

Region Covered

APAC, Europe, North America, South America, and Middle East and Africa

Key companies profiled

AB SKF, Advantech Co. Ltd., American Superconductor Corp., Avnet Inc., Bachmann electronic GmbH, Baker Hughes Co., Emerson Electric Co., ENERCON GmbH, Envision Group, Flender GmbH, Fluke Corp., General Electric Co., Hansford Sensors Ltd., HIMA Paul Hildebrandt GmbH, ifm electronic gmbh, James Fisher and Sons Plc, Nordex SE, NSK Ltd., ONYX Insight, Robert Bosch GmbH, Siemens AG, TUV Rheinland AG, and Yokogawa Electric Corp.

Key Market Trends Fueling Growth

The wind turbine condition monitoring market is witnessing significant growth due to the increasing adoption of remote and e-monitoring systems. These systems enable continuous observation and analysis of turbine performance from a central location, providing a cost-effective solution. The shift towards offshore wind turbines, which are often located in remote areas, necessitates the use of remote condition monitoring systems. Key players in the market, such as General Electric Co., offer digital and remote condition monitoring solutions for onshore wind turbines, ensuring their optimal performance and reducing downtime. For instance, General Electric Co.’s remote monitoring systems keep onshore wind turbines online, servicing major systems. Moreover, e-monitoring systems provide fast internet facilities, increasing monitoring efficiency. In February 2022, Baker Hughes Co.’s remote monitoring system identified a broken tooth in an onshore wind turbine’s gearbox, preventing potential breakdowns. These factors will fuel the growth of the global wind turbine condition monitoring system market during the forecast period. 

The Wind Turbine Condition Monitoring System market is experiencing significant growth due to the increasing focus on renewable energy and reducing greenhouse gas emissions. Offshore wind farms are a key trend, with the need for intelligent turbines and advanced monitoring systems to optimize power generation and reduce downtime. Key components for monitoring include gearboxes, which can be costly to replace, and vibration sensors for fault detection. Decarbonization and the shift away from fossil fuels are driving private sector investment in this sector. Advanced technologies like 3D printing and automation are being used to improve component manufacturing and reduce maintenance costs. Remote monitoring and autonomous inspection vessels enable real-time data acquisition and analysis, leading to improved productivity and power production. The market can be segmented into component-based and application-based, with vibration monitoring, temperature monitoring, and shaft power measurement being common applications. The reduction of carbon footprint and maintenance cost reduction are major benefits, making wind energy an affordable alternative energy source for meeting electricity demand and addressing climate change concerns. Key trends include shaft torque, RPM, torsional vibration, and shaft signature monitoring, as well as gearbox installation and high-speed output shaft condition monitoring for generator condition assessment. 

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Market Challenges

Wind turbine condition monitoring systems are essential for optimizing the performance and longevity of wind energy infrastructure. However, the cost of installing and maintaining these systems is significant. The expense includes procurement of hardware such as sensors and data acquisition systems, as well as software for data analysis and predictive maintenance. The integration of real-time analysis and time series data requires high-quality, expensive software. Additionally, security features like encryption, user authentication, and accessibility controls add to the cost. A simple wind turbine condition monitoring software costs between USD8,000 and USD16,000, while an average system costs USD16,000 to USD25,000. Complex systems involving SCADA, remote monitoring, AI, and ML cost around USD30,000 initially. The installation process is costly and time-consuming due to logistical challenges and potential modifications to existing turbines. The cost depends on factors like function set, user experience, and design requirements, which may hinder market growth.The Wind Turbine Condition Monitoring System market is witnessing significant growth due to the global push towards decarbonization and the increasing demand for affordable renewable energy. However, challenges such as autonomous inspection vessels for offshore wind farms, automation for fault detection, and high maintenance costs remain. Shaft power measurement, vibration, and temperature monitoring are crucial for maintaining productivity and reducing downtime. Remote monitoring and real-time data analysis are essential for optimizing site performance and reducing carbon footprint. Wind farm construction and private sector investment in onshore and offshore wind energy require durable turbines with high-speed output shafts and generators in good condition. Permanent condition monitoring systems help reduce maintenance costs by detecting issues in real-time, such as shaft torque, RPM, torsional vibration, and shaft signature. Wind plant flows and turbine layouts are essential factors in selecting the right condition monitoring system, with plug-and-play models and multimegawatt power ratings becoming increasingly popular for larger turbines.

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Segment Overview 

This wind turbine condition monitoring system market report extensively covers market segmentation by

Type 1.1 Equipment1.2 SoftwareApplication 2.1 Onshore wind turbines2.2 Offshore wind turbinesGeography 3.1 APAC3.2 Europe3.3 North America3.4 South America3.5 Middle East and Africa

1.1 Equipment-  The Wind Turbine Condition Monitoring System market is growing due to the increasing demand for renewable energy and the need for efficient energy production. These systems help identify potential issues before they cause significant damage, reducing downtime and maintenance costs. They use sensors to collect data on turbine performance and provide real-time analysis, allowing for timely interventions. Companies are investing in advanced technologies like IoT and AI to enhance system capabilities and improve overall performance.

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Research Analysis

The Wind Turbine Condition Monitoring System market is witnessing significant growth due to the increasing focus on renewable energy and the need to reduce greenhouse gas emissions from fossil fuels. Offshore wind farms are a major contributor to this market, as they generate a large amount of electricity and require advanced monitoring systems to ensure optimal power generation. Intelligent turbines, which use data acquisition and autonomous systems for fault detection and maintenance, are becoming increasingly popular. Key features of these systems include real-time data analysis, remote monitoring, and automation. Shaft power measurement, vibration monitoring, temperature monitoring, and shaft torque and RPM analysis are essential components of these systems. Durable turbines and affordable renewable energy are crucial for reducing downtime and meeting electricity demand. Wind plant flows and turbine layouts are also important considerations for effective condition monitoring. Climate change and the need to reduce carbon emissions are driving the demand for these systems, which help improve wind turbine productivity and reduce maintenance costs. Torsional vibration and shaft signature analysis are also critical for ensuring the longevity and efficiency of wind turbines.

Market Research Overview

The Wind Turbine Condition Monitoring System (WT CMS) market is a vital component of the renewable energy sector, focusing on optimizing wind turbine productivity and ensuring the sustainable operation of wind power generation. With the global shift towards greenhouse gas emissions reduction and decarbonization, the demand for WT CMS is surging, particularly in offshore wind farms where carbon emissions from fossil fuels are significantly higher. WT CMS utilizes intelligent turbines, data acquisition, and real-time monitoring to enhance power generation, minimize downtime, and reduce maintenance costs. Advanced technologies like 3D printing, autonomous inspection vessels, and automation are transforming the component-based and application-based markets. Key components of WT CMS include gearboxes, vibration sensors, temperature monitoring, shaft power measurement, and fault detection systems. These technologies enable the early identification of potential issues, ensuring the longevity and durability of wind turbines. WT CMS plays a crucial role in the transition to affordable renewable energy, addressing electricity demand while reducing the carbon footprint and reliance on natural resources and alternative energy sources. The market encompasses onshore and offshore wind farms, wind farm construction, and private sector investment, all contributing to the growth of the wind energy industry.

Table of Contents:

1 Executive Summary
2 Market Landscape
3 Market Sizing
4 Historic Market Size
5 Five Forces Analysis
6 Market Segmentation

TypeEquipmentSoftwareApplicationOnshore Wind TurbinesOffshore Wind TurbinesGeographyAPACEuropeNorth AmericaSouth AmericaMiddle East And Africa

7 Customer Landscape
8 Geographic Landscape
9 Drivers, Challenges, and Trends
10 Company Landscape
11 Company Analysis
12 Appendix

About Technavio

Technavio is a leading global technology research and advisory company. Their research and analysis focuses on emerging market trends and provides actionable insights to help businesses identify market opportunities and develop effective strategies to optimize their market positions.

With over 500 specialized analysts, Technavio’s report library consists of more than 17,000 reports and counting, covering 800 technologies, spanning across 50 countries. Their client base consists of enterprises of all sizes, including more than 100 Fortune 500 companies. This growing client base relies on Technavio’s comprehensive coverage, extensive research, and actionable market insights to identify opportunities in existing and potential markets and assess their competitive positions within changing market scenarios.

Contacts

Technavio Research
Jesse Maida
Media & Marketing Executive
US: +1 844 364 1100
UK: +44 203 893 3200
Email: media@technavio.com
Website: www.technavio.com/

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SOURCE Technavio

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JAMS Launches AI for Enterprise Job Scheduling: JAX and JAMS MCP, on the Model You Choose

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A new AI agent and an open-standard connector let IT teams query, diagnose, and manage automation in plain language, on the model they choose, with operational data staying inside their own network

LONDON, July 24, 2026 /PRNewswire/ — JAMS Software, an orchestration solution for scheduled and event-driven automation, today announced the general availability of two AI capabilities for enterprise job scheduling: JAX, an AI agent built into the JAMS Web Client, and JAMS MCP, a connector built on the open Model Context Protocol standard that brings JAMS into external AI coding tools. Both capabilities ship at no additional cost as part of JAMS Web.

Automation environments grow faster than the teams that run them. Jobs multiply across SQL Server, Azure Data Factory, Airflow, SAP, JDE, and Banner, and when one fails, finding the root cause often means searching several consoles at once, frequently outside business hours. At the same time, IT leaders carry pressure to adopt AI while staying accountable for where operational data goes. JAX and JAMS MCP close both gaps together.

Full details on how JAX and JAMS MCP work, including the control model behind every action, are available at jamsscheduler.com/product/ai.

JAX is an AI agent that runs inside the JAMS Web Client. It finds jobs, troubleshoots failures, and answers how-to questions in plain language, with each response grounded in the JAMS user guide and checked against a built-in glossary. JAX acts only when a user asks it to. Reads flow freely, and every write action pauses for the user’s explicit approval before it runs. JAX does not learn between sessions, and conversations are not retained on the server.

JAMS MCP is a connector, built on the open Model Context Protocol standard, that brings JAMS into the AI tools engineering teams already use, including Cursor, VS Code with Copilot, Claude Code, Claude Desktop, and Codex. Users query jobs, investigate failures, and manage runs in plain language without leaving their tool.

Both capabilities run inside the customer’s own network and act as the signed-in user, with that user’s exact JAMS permissions. There is no elevated AI account: whatever a user cannot do in the JAMS interface, JAX and JAMS MCP cannot do on that user’s behalf. Every JAX and MCP operation is recorded in its own dedicated log, and changes made through the JAMS API land in the JAMS audit trail like any other change. Customers choose their own AI model, whether a commercial provider such as OpenAI or Anthropic or a model running entirely on their own hardware, and JAMS never trains on customer data. In the current release, neither feature edits or deletes a job, folder, schedule, or agent definition. For teams that must keep operational data within a defined boundary, JAX runs on a local model entirely inside the customer’s own network, so nothing leaves at all.

“Adopting AI usually means giving something up, most often visibility into where your data goes,” said Pete Hegland, Chief Executive Officer of JAMS Software. “We built JAX and JAMS MCP so that trade does not have to happen. Every action runs as the signed-in user, every change waits for approval, and the model itself can run entirely inside your own network.”

“IT teams across the United Kingdom and EMEA tell us the same thing: they want the benefit of AI without losing sight of where their data goes,” said Greg McLaughlin, Account Executive for EMEA at JAMS Software. “JAX and JAMS MCP let them keep operational data inside their own network and still get answers in plain language. That combination is what makes this practical for the teams I work with.”

JAX and JAMS MCP are available now to all JAMS Web customers across the United Kingdom and EMEA, with no separate licence, SKU, or additional cost. AI-assisted creation of new jobs and workflows from a plain-language description is on the roadmap for a future release, gated by the same approvals and permissions as every other action.

Learn how JAX and JAMS MCP work at https://jamsscheduler.com/product/ai.

Fast facts

JAX is an AI agent built into the JAMS Web Client for job scheduling and workflow automation.JAMS MCP is a connector built on the open Model Context Protocol standard, for Cursor, VS Code with Copilot, Claude Code, Claude Desktop, and Codex.Both act as the signed-in user, with that user’s exact JAMS permissions, and there is no elevated AI account.Customers choose the AI model, including a local model that runs entirely inside their own network.JAMS never trains on customer data.Both are available now at no additional cost as part of JAMS Web.

About JAMS Software

Founded in 1987, JAMS Software is an orchestration solution that helps IT teams centralize, automate, and manage scheduled and event-driven jobs across complex, hybrid environments. Over 850 customers rely on JAMS to run their automated workloads. JAMS Software, LLC is headquartered at 108 Patriot Drive, Suite A, Middletown, DE 19709.

Media Contact
Bobby Schmidt, Vice President of Marketing
press@jamssoftware.com
800.261.4267

 

 

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Video: CNPC offers green chemical answer

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BEIJING, July 24, 2026 /PRNewswire/ — A news report from chinadaily.com.cn:

Located on the edge of the Taklamakan Desert in Northwest China’s Xinjiang Uygur autonomous region, the Tarim 1.2 MTA Phase II Ethylene Project and its supporting green and low-carbon demonstration facility of PetroChina Dushanzi Petrochemical Company, a subsidiary of China National Petroleum Corporation, are offering a new example of China’s low-carbon industrial transformation.

Watch the video to discover how CNPC is exploring a cleaner and more circular future for the industry.

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SOURCE chinadaily.com.cn

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Shanghai Electric showcases embodied intelligence robot matrix and AI-native smart factory solutions at WAIC 2026

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Featuring humanoid robots with 41 degrees of freedom, pipe‑inspection robots with ±1mm positioning accuracy, and 51 industrial‑grade AI agents

SHANGHAI, July 24, 2026 /PRNewswire/ — Operations in high-end equipment manufacturing often involve confined spaces, complex objects, and fine manipulation tasks that demand sustained and stable precision. At the recent 2026 World Artificial Intelligence Conference and High-Level Meeting on Global AI Governance (WAIC 2026), Shanghai Electric (SEHK: 02727, SSE: 601727) showcased its comprehensive portfolio of embodied intelligence solutions tailored to a range of industrial scenarios.

Themed “AI for All: Smart Squad, Shining Without Limits,” Shanghai Electric highlighted its capabilities across embodied AI robots, robot core components, and AI-native smart factory solutions, demonstrating end-to-end capabilities spanning complete robot systems, critical parts, industrial software, and smart factory architecture.

“The true value of embodied intelligence lies in understanding real industrial tasks: combining the strength, precision, and stability of machines with human experience and judgment to drive a genuine paradigm of ‘machine-assisted, human-machine collaboration,'” said Wang Chunlei, deputy general manager of the Robotics Business Unit at Shanghai Electric Automation Group.

Shanghai Electric’s robotics portfolio covers five key industrial scenarios: connector insertion, electrical operations, flexible sorting, intelligent assembly, and pipe processing. Highlights include:

“SUYUAN” bipedal humanoid robot: With 41 degrees of freedom for enhanced mobility, it is equipped with a multimodal visual sensing system on the head and torso, along with a dual-battery hot-swap system. It is well-suited for inspection, material handling, and assembly tasks.”TUOYUAN” industrial wheeled humanoid robot: Powered by an embodied intelligence foundation model and force-position hybrid control, it is capable of multi-spec connector insertion, material sorting, and loading/unloading of automotive sheet metal parts.”Mermaid” bionic wheeled humanoid robot: Capable of autonomously identifying buttons, knobs, and air switches, it generates real-time operation paths.Autonomous pipe inner-wall chamfering robot: Designed for confined spaces, it can position and process thousands of hole edges with accuracy within 1 millimeter while transmitting data in real time.

Shanghai Electric also showcased its portfolio of core components ranging from power-output to end effectors. Among them, the planetary roller screw offers more than three times the load capacity of traditional ball screws, while the DexHand dexterous hand is designed to meet diverse gripping and manipulation requirements.

Shanghai Electric launched 51 AI models and agents under its “StarCloud Intelligent Manufacturing” series across three domains: R&D and design, production and manufacturing, and operations and maintenance—covering critical equipment processes such as process optimization and wind power facility maintenance.

These industrial agents are embedded in robotic decision-making systems and the operational logic of AI-native smart factories, transforming industrial expertise into digitized, reusable capabilities. They support production-line scheduling, quality inspection, and predictive maintenance, driving the evolution of manufacturing systems from experience-driven to data-driven operations.

Shanghai Electric also released the “AI-Native Smart Factory Technology White Paper,” proposing an active evolution architecture that enables real‑time, closed‑loop optimization of production data, giving the factory self‑perception, self‑decision, and self‑execution capabilities. Built on First Principles, the AI‑native smart factory vertically integrates process flows, industrial software, agents, and smart equipment to dismantle traditional hierarchies while horizontally bridging data silos. The architecture features three core layers: the AI factory brain as the “control center,” industrial agents and embodied robots as the “execution network,” and the physical twin as the “digital mirror.”

Leveraging its deep industrial expertise and comprehensive solution capabilities, Shanghai Electric will continue to drive the implementation of AI in industrial settings, tackle technical challenges facing embodied intelligence in complex scenarios, accelerate the large‑scale deployment of AI‑native smart factories, and deliver replicable solutions across diverse manufacturing environments.

SOURCE Shanghai Electric

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