Monitor and improve the efficiency of production processes (challenge retrieved from the literature).
Challenge:
Wind energy can play a critical role in the transition to renewable and green energy. Wind turbines are subjected to various environmental conditions, wear and tear, and mechanical stresses that can affect their efficiency and lead to unexpected failures and costly downtime. Implementing Industry 4.0 solutions can address this challenge by enabling efficiency maximization and predictive maintenance. The challenge is to develop a system that leverages IoT sensors, data analytics, augmented reality, and machine learning to predict when specific components of a wind turbine are likely to fail, allowing proactive maintenance and reducing operational costs.
Main Requirements:
- Digital Twin Development: create highly detailed replicas of wind turbines, incorporating real-time data from sensors, maintenance history, and environmental conditions
- Predictive Maintenance: use AI and ML to predict potential maintenance needs, including components wear and potential failures
Other Requirements:
- Augmented Reality for remote inspection and maintenance guidance
- Energy Yield Optimization for dynamically adjusting turbine operation parameters
Industry Sector:
Wind Energy
Challenge classification:
Real-time Performance and Process Monitoring; Maintenance; Operations Planning and Scheduling.
Time for Project Completion:
12 months.
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Preliminary Analysis:
The solutions to the challenge can adopt techniques and methods coming from the following subfields.
Data Collection Methods:
The data collection process can be solved mainly via IoT sensors data and data acquisition systems. The specific technology used for this process can vary, but usually there can be problems in different platforms communication and integration. The choice of Zerynth devices can solve this issue and its seamless integration with other adopted technologies can save a huge amount of work and time.
Data Analysis Techniques:
Regarding data analysis, the proposed solution suggests the use of Deep Learning. The field of data analysis received a lot of attention from the academic world and an enormous amount of techniques and algorithms have been proposed in the last decades. However, DL models seem the most flexible and allow users to handle different data sources. On the contrary, other AI algorithms are usually tailored for specific solutions and are capable of processing a single data type (like images, or tabular data). Deep learning, and specifically multimodal deep learning models, instead, can combine knowledge from different sources and provide (usually) better results. This feature can be a crucial one in this challenge, since the monitoring of surrounding environment and turbine statuses can possibly be carried out with cameras.
User-Centric Delivery of Insights:
As well as the previous ones, this field has been very well studied since the beginning of the digital era. Other than Virtual Reality, a variety of techniques can be used for delivering insights from collected data. Among them, most of them do not provide a full remote usability or can be too difficult to implement. For example, Augmented Reality and Mixed Reality allow for interaction between a final user and the surrounding environment. However, the first one requires physical access to the desired environment and does not support fully remote usability, while the second one can be difficult to reproduce because a full physical simulation of the environment is needed. VR can be a suitable compromise between simulation and full remote usability.
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Solution Summary:
Our solution adopts a robust infrastructure that harnesses the capabilities of Time Series Databases, Zerynth’s versatile hardware, and the visualization power of Grafana. Time Series Databases seamlessly manage the temporal data required for monitoring equipment health and performance. Zerynth’s hardware not only serves as a flexible data collection tool but also reduces downtime and maintenance expenses. Grafana complements this data-driven approach by offering real-time insights and alerting functionalities.
Regarding the predictive maintenance and immersive visualization part of the challenge, our solution employs virtual reality (VR) and deep learning (DL) as transformative tools. VR’s immersive, three-dimensional visualization capability provides a comprehensive understanding of intricate machinery and is particularly valuable for maintenance and training. The integration of VR with IoT sensors enables real-time monitoring and proactive maintenance. Deep Learning, on the other hand, introduces a higher degree of accuracy and early anomaly detection in predictive maintenance scenarios. Multimodal DL models enhance these capabilities by aggregating insights from diverse data sources.
Additional Benefits:
- Energy Efficiency: the solution will lead to reduced energy consumption, contributing to cost savings and environmental sustainability.
- Marketing Improvement: the huge hype in both renewable energy sources and machine learning, can severely boost the appeal of the company in the market.
Possible Difficulties:
- Highly-skilled employees: all the proposed milestones require educated workers, with hard skills in Computer Science, Engineering, and Data Analysis. Finding these highly-specialized professional figures can be a very difficult task.
- Development bottlenecks: the more advanced features of the proposed solutions must be built upon a very efficient and reliable data acquisition system. Any problem and missed milestone of the initial phase will slow down the development of the most advanced ones.
- User Adoption: promoting the adoption of data-driven decision-making among users may encounter some reluctance. Addressing this hurdle necessitates the implementation of effective training and change management approaches, which can be particularly demanding in the industrial sector due to its unique intricacies.
Sources:
[1] E. D. I. Consortium, “Remote measurements control | EDI – european data incubator.”
[2] https://powerbi.microsoft.com/
[3] https://grafana.com/
[4] STMicroelectronics, “Predictive maintenance at LACROIX Electronics.”
[5] S.Heo et al., “Data-Driven Hybrid Model for Forecasting Waste water Influent Loads Based on Multimodal and Ensemble Deep Learning.”
[6] Libelium, “Smart Industry 4.0 technology – industrial internet of things (IIoT) libelium.”
[7] https://zerynth.com/
[8] M.Group, “5G technology: Enabling the IoT, AI and Industry 4.0 – research | merck global.”