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    Mostly Used By The Manufacturing Industry

    OpenAI Application for Predictive Maintenance

    Predictive maintenance (PdM) is a data-driven approach to maintenance that uses historical data to predict when equipment is likely to fail. We leverage ML models developed by OpenAI, as well as OpenAI's language models to extract insights from text data.

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    How It Works?

    It is important to understand the high-level architecture to get a clear idea how we leverage OpenAI capabilities while building a predictive maintenance system.

    Data Collection & Preparation

    In this phase, we leverage tools like Azure Data Factory, Azure Blob Storage, Azure Databricks or Azure Synapse Analytics to store, manage and transform the data collected. We have collected data from various sources, such as sensors, equipment logs, historical maintenance records, and external databases. The data collected includes variables like temperature, pressure, vibration, power consumption, and other relevant parameters.

    Model Training Using OpenAI

    We have developed this Predictive Maintenance Model using the advanced technology provided by OpenAI. By harnessing the power of OpenAI's algorithms and leveraging their cutting-edge techniques, our model proactively predicts equipment failures, enabling organizations to optimize maintenance schedules, reduce costs, and maximize operational efficiency. With a focus on accuracy and reliability, our solution integrates the robust capabilities of OpenAI, ensuring a reliable and effective predictive maintenance system for our clients.

    Model Deployment

    We prefer Azure Services like Azure Functions, Azure Kubernetes Services (AKS), or Azure App Services to deploy the Contact Center Analytics model, but the same model will also be able to deploy on other cloud platforms like AWS Services e.g; Amazon SageMaker, Amazon Elastic Compute Cloud (EC2), Amazon S3 (Simple Storage Service) AWS Lambda, for GCP Services like Google Compute Engine, Google Cloud Functions, Google Cloud AI Platform can be used, HuggingFace etc.

    What We Offer

    ThirdEye Data develop predictive maintenance system from scratch. We implement the required ML models developed by OpenAI. These algorithms get trained on historical data to identify patterns that can be used to predict future failures.

    OpenAI Integration

    Leveraging ML algorithms developed by OpenAI and training them on historical to deliver tailored predictive maintenance solutions.

    Data Transformation

    Taking care of the data collection, cleaning, transforming, and preparing the sensor data log and historical data at scale to train the ML model.

    Model Development

    Develop custom machine learning model including feature selection, algorithm and techniques to increase model result accuracy.

    Request A Demo

    We have prepared custom Predictive Maintenance ML models which are ready to be deployed.

    Customer Feedbacks

    All the reviews are independently verified by Clutch.

    ThirdEye Data brought in their best to support us.

    Kumar Mankala Advisor of AI/ML Program & Products, SCE

    They went above and beyond to actualize the our ambitions.

    Matt Singer CEO, EcoTrak

    Their project management and communication were top-notch.

    Andriy Romanyuk Head of R&D, Glas Trösch Group

      Frequently Asked Questions

      Before enterprises opt for predictive maintenance, they may have some common asks. We are trying to answer them here.

      The key features of a predictive maintenance model will depend on the specific data that is available and the business goals of the model. However, some common features include:

          • The age of the asset: The age of an asset can be a good predictor of when it is likely to fail.
          • The number of hours it has been used: The number of hours that an asset has been used can also be a good predictor of when it is likely to fail.
          • The condition of its components: The condition of the components of an asset can also be a good predictor of when it is likely to fail.

      It is common to get stuck with an AI initiative or project. To avoid the same, consider these points at the very begining:

      Your budget: How much are you willing to spend on a sentiment analysis platform?

      Your needs: What do you need the platform to do?

      The accuracy of the platform: How accurate is the platform?

      The ease of use of the platform: How easy is the platform to use?

      The support offered by the engineering team: What kind of support does the engineering team offer?

      The budget for the project will depend on the specific scope of the project. However, in general, predictive maintenance projects can be relatively expensive.

      The data we feed to a predictive maintenance model will vary depending on the specific assets that are being monitored. However, some common data sources include:

          • Sensor data: Sensor data can be used to track the performance of assets and identify potential problems.
          • Maintenance logs: Maintenance logs can be used to track the history of maintenance activities and identify trends.
          • Historical failure data: Historical failure data can be used to identify the factors that are most likely to cause an asset to fail.

      The business goals of a predictive maintenance model will vary depending on the specific needs of the organization. However, some common goals include:

          • Reducing downtime: By predicting when an asset is likely to fail, preventive maintenance can be scheduled before a failure occurs. This can help to reduce downtime, which can save businesses a significant amount of money.
          • Improving asset reliability: Predictive maintenance models can help to identify and address potential problems before they cause a failure. This can help to improve the reliability of assets, which can lead to increased productivity and profitability.
          • Optimizing maintenance schedules: Predictive maintenance models can help to optimize maintenance schedules by identifying the most critical assets and scheduling maintenance accordingly. This can help to reduce costs and improve the efficiency of maintenance operations.

      We offer end-to-end predictive maintenance solution. It means we not only develop the model from scratch but also take full responsibility of maintaining, monitoring and retraining the model.