Support solutions for the construction of machine records and maintenance management of equipment.
Digitize equipment information, failure history, reports, etc., and create equipment records. Establish the basis for calculating LCC and operating rates, re-evaluate maintenance methods, and create system requirements.
Many companies have proposed maintenance methods utilizing large-scale data and AI (artificial intelligence/machine learning), and there are numerous reports on their effectiveness. However, with a few exceptions, the management of maintenance on-site is primarily conducted using various formats such as paper, Excel, Access, and PDF, and the information managed differs by department. Our company provides services to address the following objectives through the construction of equipment records: 1. We want to organize the ledger of data that serves as the foundational requirements for the introduction of a maintenance system. 2. We want to utilize the accumulated failure information on-site to establish inspection cycles that minimize costs. 3. We want to consider maintenance methods tailored to the situation, as even the same equipment may have different usage conditions and environments. 4. We want to create a risk matrix from accident and failure information. 5. We want to graph the relationship between maintenance items, reliability, and costs to serve as a guideline for planning. The steps for constructing equipment records are as follows: 1. Digitization of various information. 2. Organization and classification of the digitized information. 3. Implementation of various analyses according to objectives. 4. Addition of management items based on the analysis results.
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basic information
1) Data digitization of various information We convert your existing data (in various formats such as paper, Excel, PDF, Access, etc.), including equipment information, failure history, and reports, into electronic data for centralized management. Initially, we will manage this data in an Excel or Access format for easier handling later on. 2) Organization and classification When converting multiple pieces of information into electronic data, inconsistencies may arise, such as duplicate management items or different wording that conveys the same meaning. Among these inconsistencies, the following items are particularly important: - Failure/accident information - Emergency measures - Permanent measures - Impact when a failure/accident occurs Here, we will eliminate duplicate information and classify and code the above items to organize the data necessary for analysis. 3) Analysis We select appropriate methods based on the objectives and conduct the analysis. Typical methods include: - Weibull analysis - Bayesian statistics - MCMC method - Text mining - Machine learning - Reliability assessment 4) Addition of management items Based on the results of the analysis and the objectives, we will add any necessary or missing items.
Price range
P5
Delivery Time
※3 months～
Applications/Examples of results
We have a track record in the following industries: - Petrochemical plants - Nuclear power generation - Electric wires - Aerospace - Railways - Assembly manufacturing
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Company information
Our company develops and sells a "Maintenance Management System" for managing and operating various plants, factories, and other facilities and assets. Currently, this system is undergoing significant evolution into one that incorporates IoT technologies, such as sensor information and input from tablet devices, as well as AI technologies like machine learning, featuring functions for failure prediction and automatic scheduling. Additionally, as part of the recent trend towards digital transformation (DX), there is a growing movement to digitize and automate manufacturing processes and research and development sites in factories to enhance operational efficiency. In line with this trend, our company provides a solution aimed at improving efficiency in research and development environments, known as the Laboratory Information Management System (LIMS), which includes features such as workflow management, data tracking, data management, data analysis, and integration of electronic lab notebooks.
