Blog

14 Mar

RLAD Case Study

One of the biggest challenges is the rarity and unpredictability of anomalies and failures, resulting in insufficient data to build a complete and accurate failure prediction model in real-world scenarios. In addition, the need for multiple models to address the different types of devices in a single plant can make operations and maintenance challenging. This is because these multiple models operate under different assumptions about the underlying patterns in each device’s data set.

To address these challenges, our team has developed an RL-based solution with the following features:

The RL-based solution is designed to learn more effective anomaly detection strategies with a feasible amount of data.
The solution employs a single anomaly detection model for monitoring the health of the entire factory, reducing the complexity of operations and maintenance.

The RLAD agent uses plant state as observations to predict anomaly scores for early detection of potential failures. The reward system is designed to avoid false detections and the agent’s predictions are used to indicate the plant’s health and root cause of abnormality. This helps operators schedule maintenance and minimize impact on production efficiency. The RLAD can also be applied to quality control and process monitoring in manufacturing.

Thus, reinforcement learning is a promising approach for solving manufacturing problems. Its ability to handle complex, dynamic systems and make decisions based on uncertain and changing information makes it well-suited for applications in production scheduling, predictive maintenance, and inventory control.

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08 Mar

Implementation of Reinforcement Learning in Manufacturing

Software Team

Reinforcement learning is a subfield of artificial intelligence that focuses on enabling an agent to make decisions in an environment by continuously taking actions, observing the consequences, and learning from the rewards it receives for its actions. The ultimate goal of the agent is to learn a policy that maximizes its cumulative reward over time. In recent years, there has been growing interest in using reinforcement learning to address manufacturing problems.

We have recently applied RL in anomaly detection. Anomaly detection in manufacturing involves identifying any unusual or unexpected behavior in a production system, which can indicate potential problems such as equipment malfunctions, process disruptions, or quality issues. The goal of anomaly detection is to identify these deviations as early as possible so that corrective actions can be taken to minimize the impact on production and prevent costly downtime.

In RL-based anomaly detection, the agent acts as a detector and learns to identify anomalies in the factory process data through trial-and-error. The agent’s performance is evaluated based on its ability to accurately identify anomalies and receive rewards for successful detections. The agent’s policy for detecting anomalies is constantly updated and improved as it interacts with the data, making it capable of detecting anomalies in different types of devices within the factory. Thus, the agent serves as a single anomaly detector for the entire factory.

The reward equation in RL-based anomaly detection incentivizes the agent to predict anomalies as early as possible while minimizing false positive detections. The health of the factory process is indicated by the range of predicted anomaly scores, allowing the operator to quickly identify any deviations from the normal process. The root cause of these deviations is also displayed, providing the operator with crucial information to take the necessary corrective actions.

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02 Mar

Mechanistic Modelling & Problem Solving- A new Data-Driven CAE Process

The automobile industry is constantly evolving and adapting to the fast-paced market demands. To stay ahead of the competition, OEMs are focused on reducing their product development cycle and bringing new vehicles to market in record time. With the help of cutting-edge PLM solutions, the once lengthy 36-month process has now been trimmed down to just 24 months. However, the quest for accelerating product development does not stop there, Auto OEMs are continually exploring new approaches and techniques to achieve the ultimate goal of 12-month development cycle.

This is where EinNel Technologies comes into play, offering a new-age design approach called Data-Driven CAE. The combined expertise of traditional CAE and Data Science inspired the team to leverage the large volumes of data available across the functions right from the concept stage to final production to make predictions about product performance, resulting in a more informed and optimized design process.

Data-Driven CAE prioritizes a reality-driven design rather than a simulation-driven design that takes tooling and manufacturing constraints into account from the very beginning, with the goal of reducing the vehicle design and development cycle by 50%. This eliminates the need for multiple iterations for each design release and provides the most accurate results at the early stage of the design phase by incorporating machine learning and big data solutions.

Based on this approach, we are currently developing a cloud-based platform called EinNel MDOX that connects engineers, data, and resources to effectively model and optimize vehicle designs using real-time data from simulations, physical tests, and field tests. By harnessing the full potential of the cloud, organizations no longer need to rely on high-end computing hardware and validate complex designs with less computing power, allowing engineers to work from anywhere and anytime.

We believe that EinNel MDOX has the potential to revolutionize the way vehicles are designed and pave the way to reducing the vehicle design and development cycle to 12 months.

Mechanistic Problem Solving:

The Data-Driven CAE can be further scaled and redefined as the Mechanistic Solution Approach for solving complex engineering problems by integrating numerical, analytical, and statistical methods. Our technical team has been closely working with various Auto OEMs to identify and resolve vehicle manufacturing challenges using mechanistic solutions.

The conventional method of problem-solving is often time-consuming and resource intensive as it requires extensive domain expertise and experience. In contrast, the Mechanistic Solution Approach provides a faster and more efficient method of problemsolving, delivering results that are accurate and reliable by leveraging our advanced analytics engines.

One of the primary challenges faced by the automotive industry is developing solutions that are compatible with their ecosystems. With a deep understanding of the PLM tools, CAD/CAE software, and ERP products used by the industry, EinNel Technologies is well-equipped to develop solutions that are perfectly suited to the needs of the OEMs’ existing global ecosystems. Also, we understand the importance of delivering solutions that are both effective and easy to use. That is why we have taken the extra step to package our mechanistic solutions into user-friendly applications with interactive dashboards powered by ML algorithms. The dashboards can be tailored to fit the unique requirements of every stakeholder, allowing them to effectively utilize the data and insights we offer. Whether they are looking to improve their manufacturing processes, optimize their designs, or gain a deeper understanding of their product performance, our dashboards give them the tools they need to succeed.

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06 Jan

Our Technology Focus – 2023

Albert E George, CEO of EinNel Technologies

During our recent town hall meeting, we unveiled our tech trends for 2023. At EinNel, we regularly reassess and adjust our technology focus to ensure that we are staying up to date with the latest and most advanced technologies in the field.

With a vision to help industries fully embrace and implement Industry 4.0 and by realizing the need to incorporate Artificial Intelligence into our solutions as it is increasingly prevalent in our daily lives, we are pivoting to the following technologies:

Platform Engineering
Artificial Intelligence of Things (AIoT)

Platform Engineering:

Platform engineering involves the design and development of data-driven platforms that can be used by industries to digitize their operational workflows and build a continuously growing knowledge base with the help of artificial intelligence.

EinNel drives the automotive and manufacturing industries toward streamlining their product development process through the use of platform engineering. By centralizing their distributed functions under a single platform, EinNel enables these industries to reduce the number of iterations required and improve data management. This allows for quick and easy access to data, which can greatly accelerate product development.

To promote the vision of our data-driven platforms across industries, we have devised a set of product philosophies. Some of them are listed below:

Immortalizing Engineering Data - Data must be preserved and continually leveraged to support future growth
Propel data at rest into motion - Data at rest is simply potential energy waiting to be harnessed and put into motion to drive progress and innovation
Make your data easy to query - Well-organized and easily accessible data is the key to unlocking its full potential and driving powerful insights
Live Dashboards in place of reports - Real-time, interactive dashboards provide a more dynamic and engaging way to visualize and understand data, replacing the static nature of traditional reports
Confluence data under one platform - Unifying an organization’s data onto a single platform enables more comprehensive and actionable decision-making

As we enter the new year, our technical team is committed to utilizing platform engineering techniques to reduce the time it takes to bring new products or services to market and continuously optimize industrial operations to better serve the customers.

EinNel AIoT Hub:

Artificial intelligence (AI) is becoming an increasingly integral part of our dailylives. From virtual assistants to self-driving cars and medical diagnosis to financial analysis, AI is transforming the way we live and work.

Recognizing the necessity of integrating Artificial Intelligence into our solutions, EinNel has decided to set up a dedicated technology hub that will bring together a team of experts, including Data Scientists, AI and ML Engineers, Domain Experts, Scientific Software Developers, Cloud Architects, and Data Engineers to build a cloud-ready AI infrastructure and unlock the business value from industrial data. By establishing this hub, we aim to stay at the forefront of technological advancements and continue to provide cutting-edge solutions to our clients.

In the first phase of establishing the EinNel AIoT Hub, our focus is to implement the following AIoT capabilities by 2023.

Integrated Data Management:

Seamlessly integrate and manage industrial data from various sources in a way that allows for easy access and predictive analysis for improved decision-making and more efficient operations.

Edge and Cloud Computing:

Building a collaborative computing platform that combines the advantages of both edge and cloud computing to create and deploy AI-powered IoT applications, sophisticated operational processes, and industry compliance standards.

Production-grade AI environment:

By enabling developers and data scientists to create, test, and deploy high-quality AI models and applications at enterprise speed and scale, organizations can adopt and utilize AI technologies to drive business value and innovation.

Industrial AI applications development:

Developing fit-for-purpose, domain-specific industrial applications by combining domain expertise and first principles-based models with artificial intelligence and analytics algorithms.

In 2023, EinNel is positioning itself to play a key role in the realization of Industry 4.0 and bringing industrial functions and technology together in a way that promotes rapid and sustainable product development by focusing on Platform Engineering and Artificial Intelligence of Things.

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19 Jan

Current drift and need of AI in life science

Koushika, EinNext Biosciences

Life science is the broad spectrum that entangles all fundamental and applied sciences. It is the study of organisms at different levels and their applications. The umpteen challenges faced during the pandemic to outlook further accuracy with less time consumption can be sorted with the help of Artificial Intelligence (AI). The world which requires both efficacy and expeditious results await none. But we all know studies, experiments and research require enormous patience, skill, and understanding. Scientists and people have different opinions during emergent circumstances like a pandemic. AI comes in handy during such circumstances helping life science scientists to research and come out with quicker and better results.

The par excellent development of technologies and new breakthroughs has paved the way to upskill in the field of life science. This pandemic has not only hindered the conventional way of research but has taught us an empowering technique of associating life science with AI. AI has become the access point to incalculable data for research. The more the data flows in, the more accuracy we obtain. The ideology in various life science researches includes the whole of humankind.

The drug discovery process nearly takes 15 to 20 years to launch a particular drug in the market. This process involves huge investments, tedious work, and a large workforce with accuracy. If the drug fails to cross the pre-clinical or clinical trials the loss is unimaginable and the chances of finding a cure to that particular disease will become minimal. At this juncture, AI helps scientists to scan and verify large and complicated datasets more accurately.
Radiology is another field where AI can play a vital role wherein various simulations can be run, analyzed, and give doctors/ clinicians a better-detailed understanding of particular malformation or disease. Virtual biopsies will become possible in the upcoming years.
Antibody engineering through machine learning approaches can provide us with solutions where we can get antibodies with more affinity which will reduce the dosage of that particular antibody thus reducing the cost.
Surgeries to the most inaccessible parts of our body can be done using robots very precisely. AI programs can help the robot learn and train to operate with less or no damage.

AI-powered mobile applications to consult and manage hospitals records in developing or remote places will provide immediate connection to the doctors at the time of emergency.

The above are a few fields in which AI is needed to embrace with life science which will enhance and promote life science research to another level. There are other fields like immunology, drug designing, genetics, etc in which AI can be applied for further research and development. This sudden drift and need of AI is the most promising one for humankind but it also has few restraints. A particular framework of data collection involving various ethics has to be implemented. The requirement of a well-trained or expert workforce to handle and understand both Life science and AI is necessary.

EinNext Biosciences - the sister company of EinNel Technologies has been practicing this type of research for a few years now. Researches on antibody engineering, AI-powered drug discovery and enzyme engineering, orthopedic research especially in the knee and hip replacement, application of ML in the biomedical field for aneurysm rupture prediction, healthcare management through EinNel H+, pre-surgical knowledge through EinNel S+, and cloud management through EinNel C+ have been the current research fields at EinNext.

This advancement in life science research after a few years will become a more predominant and highly efficient one.

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