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MELSOFT MaiLab

MaiLab is a software offering a variety of machine learning and statistical analysis methods, including AI features like deep learning and multiple regression analysis. It enables data analysis for various purposes without requiring programming, making data analysis solutions easy to implement.

One software for all types of data analysis

This software alone covers the phase of analysis of production data in the office and the phase of diagnosis in production based on these analysis results, so that it is possible to apply the learning models obtained from data analysis directly online in the production.

AI data scientist - an AI-based analysis supporting system for everyone



Customer benefits:
  • Very short training phase for the software as no specialized knowledge is required, anyone can do data analytics
  • MaiLab is supporting the customer in all phases of the data analysis project
  • Customers benefit from MaiLab as many companies see a lack of manpower for data analyzing
  • Customers are empowered to improve their production efficiency quickly and efficiently

    An UI bringing you data analytics with a great experience



    Customer benefits:
    • Quick Return-on-invest as MaiLab Software is a single tool for both Offline Analysis and Real-time diagnostic including direct feedback to the production site. Rich possibilities for data visualization.
    • Longevity and future-proof design and operation of MaiLab through integrated open concepts like Python programming language or web-based environment.
    • Flexibility through different licensing schemes available (Yearly subscription for OPEX, perpetual model for CAPEX) and many different application scenarios.

    Tech Talks - MaiLab

    Analysis Process

    MELSOFT MaiLab is a tool that enables easy data analysis in 4 basic steps.

    Offline analysis

    Step 1: Data set creation

    First, read the data to be analyzed into MELSOFT MaiLab and register them. A group of registered data is called a “data set”.The data set can be shown in various kinds of graphs, so that it can also be easily checked by human eyes before performing diagnosis using AI.

    Step 2: AI creation

    Learning from the data set is performed.A model that enables diagnosis of unknown data is called “AI”.When “What you want to do (objective)” is selected, the regularity and rules of the data are automatically derived, and MELSOFT MaiLab creates the “AI”.

    Real-time diagnosis

    Step 3: Task creation

    Settings for performing diagnosis of unknown data are called a “task”.MELSOFT MaiLab will define the data input/output methods and threshold values for whether diagnostic results are good or bad. The accuracy is displayed as a score, which serves as a guideline for judgment.

    Step 4: Task execution and monitoring

    You can execute tasks and monitor the diagnosis status of unknown data.Deployment to equipment can be easily performed with just a click. Data flow and good or bad judgment status can be confirmed on a graphical display via the learning server.

    Offline Analysis

    Prepare the data. (Data set creation)

    In order to analyze the data and create the diagnosis model, it is necessary to register the data subject to analysis in MELSOFT MaiLab. A group of registered data is called a “data set”. By registering the data set, the data can be visualized in tables or graphs, and diagnosis models can be created.

    Data registration can be performed by simple mouse operations

    The original file of data to be registered as a data set is called the “data source”.Data sources which can be registered are CSV-format and TSV-format text files.

    Create diagnosis rules. (AI creation)

    Perform pre-processing of the data set and create AI by performing learning according to analysis methods.

    Interactive and easy. Automatic AI creation saves time and effort

    Automatic

    MELSOFT MaiLab selects the optimum pre-processing and analysis methods based on the objectives and data set contents, and automatically creates the AI. Select this when you don’t know what analysis method to use for what you want to do (objectives).

    Manual


    In this method, you select the analysis methods yourself and create the AI. Select this when the appropriate method for what you want to do (objectives) is clear.

    Complete the AI

    Proceed to creating the task to perform real-time diagnosis while referring to the displayed scores and comments, When learning has been completed, AI creation will be completed. You can manually change the completed AI to customize it to increase reliability.

    You can customize the AI to increase its accuracy

    In MELSOFT MaiLab, each process of AI is performed in a block, and the AI processing flow is created by connecting the blocks. You can edit the AI flow prepared by the AutoML function to freely customize it or create an original AI from scratch.

    Original processing can be performed with Python blocks

    MELSOFT MaiLab is also equipped with function expansion blocks that are useful for customizing learning models. You can also perform coding in Python, which is often used in data analysis. By performing customization, you can create learning models with higher accuracy.

    Real-time Diagnosis

    Implementing in the device (task creation)

    A group of processes (process flow) using the created AI to perform diagnosis on unknown input data and output the diagnosis results is called a “task” in MELSOFT MaiLab.* A simple task can be automatically created by setting the necessary parameters for the operation of each process.

    *There are 2 types of tasks: simple and advanced. For details, please refer to the manuals.

    When using Mitsubishi Electric FA equipment, devices can be specified directly

    MELSOFT MaiLab has high affinity with Mitsubishi Electric FA equipment.Since direct specification of compatible devices can be performed, device deployment (arrangement) can also be performed easily.

    The status is shown in real time during task execution

    Diagnosis results are shown in line graphs and pie charts.Diagnosis results and data input to AI are shown in table format.

    System Configuration/License/Operating Environment

    System Configuration

    Data collection and diagnosis can be started in MELSOFT MaiLab with just a basic license. In addition, systems can be freely configured according to the scale of facilities, increases in the number of analysis users, etc.

    License

    Operating Environment

    Learning operating environment
    In the minimum operating environment, it is possible to execute methods such as multiple regression analysis, etc. with relatively low calculation processing when no other tools are running. To execute methods such as deep learning, etc. that require lots of calculation processing, the recommended operating environment is necessary.

    ItemMinimumRecommended
    ComputerPC, industrial PC, serverPC, industrial PC, server
    CPUIntel® Core™-i3 equivalent or betterIntel® Core™-i7 equivalent or better*1
    Memory4 GB or more16 GB or more*1
    OSWindows® 10 (Pro, Enterprise, IoT Enterprise)Windows Server 2019 (Datacenter, Standard, Essentials)Windows Server 2016 (Datacenter, Standard, Essentials)Windows® 10 (Pro, Enterprise, IoT Enterprise)Windows Server 2019 (Datacenter, Standard, Essentials)Windows Server 2016 (Datacenter, Standard, Essentials)
    64-bit64-bit
    Available storage space16 GB or more64 GB or more

    Collection/diagnosis operating environment

    ItemMinimumRecommended
    ComputerPC, industrial PC, serverPC, industrial PC, server
    CPUIntel® Core™-i3 equivalent or betterIntel® Core™-i7 equivalent or better*1
    Memory4 GB or more16 GB or more*1
    OSWindows® 10 (Pro, Enterprise, IoT Enterprise)Windows Server 2019 (Datacenter, Standard, Essentials)Windows Server 2016 (Datacenter, Standard, Essentials)Windows® 10 (Pro, Enterprise, IoT Enterprise)Windows Server 2019 (Datacenter, Standard, Essentials)Windows Server 2016 (Datacenter, Standard, Essentials)
    64-bit64-bit
    Available storage space16 GB or more32 GB or more

    *1 Required when executing not just methods such as multiple regression analysis, etc. with relatively low calculation processing, but methods such as deep learning, etc. that require lots of calculation processing.