Dura Systems | Tood Data Explorer

Tool Data Explorer（TDX）

Overview

TDX is a monitoring system that uses CSV data collected from equipment to monitor for signs of equipment abnormalities and predict processing quality. It supports a variety of equipment and parameters, and can perform real-time and daily monitoring simultaneously. It can be flexibly configured for one to hundreds of units, allowing multiple monitoring settings to be made in a short amount of time.

Benefits of implementation

Early detection of equipment abnormalities:
It monitors for signs of abnormalities in real time and can output results within minutes. Daily predictive monitoring can also be performed in parallel, preventing sudden problems before they occur.
Prediction of processing quality:
By feeding back the inspection results, the processing quality of each process can be predicted, which directly leads to a reduction in defects and an improvement in yield.
Flexibility to use any equipment:
It can handle any type of equipment that can collect data in CSV format. It can handle a variety of parameters such as current, voltage, rotation speed, speed, temperature, pressure, and set values.
Supports small to large scale:
The number of monitored devices can be expanded from one to several hundred, allowing you to build a system that suits your factory size and line configuration.
Configure a large number of monitors in a short time:
Automation functions allow you to efficiently configure a wide range of monitoring settings, significantly reducing the amount of work required for setup.
Quick setup:
If you have CSV data, it can be implemented in as little as one day, and you can start monitoring right away. (CSV data collection from equipment is outside the scope of TDX support)

Key Features

Simultaneous execution of multiple monitors:
From real-time monitoring within a few seconds to daily predictive monitoring, parallel monitoring can be performed according to your needs.
Various parameter integration:
Centrally manages different indicators for each piece of equipment, such as current, voltage, rotation speed, speed, temperature, pressure, and set values.
Waveform Extraction:
Extract any waveform from the time series waveform loaded in CSV.
Product Quality Prediction:
Equipment monitoring data is integrated to predict the quality of processed products.

Waveform monitoring

A reference waveform is registered and deviations from the reference waveform are monitored.

Statistics monitoring (SPC)

Extract statistics from waveforms and perform SPC (Statistical Process Monitoring).

Multi-waveform monitoring (waveform correlation monitoring)

Monitors the time-series correlation of multiple waveforms.

TDX multi-waveform monitoring (multivariate monitoring).

Multi-statistical monitoring (multivariate monitoring)

Perform multivariate monitoring of multiple statistics.

Convenient features (convenient features)

Threshold auto-adjustment:
Automatically adjusts and optimizes monitoring thresholds.
Threshold setting update:
Export/Import the monitoring thresholds as a CSV file and update them.
Monitor Copy:
Copy waveform extraction settings and monitoring settings between multiple pieces of equipment.
Automatic monitoring.:
Auto-generate monitors by product.
System operation monitoring:
It monitors module startup status, CSV file input status, monitoring time delay, and disk overflow.

Abnormal notification

Alarm transmission with monitoring images:
When an abnormality is detected, an alarm with monitoring images will be sent via email.
Alarm file output:
When an abnormality is detected, an alarm text file and a CSV file are automatically output.

File Output

Waveform Data Output:
The extracted waveform data is automatically output to a CSV file.
Statistical Data Output:
Statistical data extracted from waveforms is automatically output to a CSV file.
SPC monitoring result output:
SPC monitoring results are automatically output to a CSV file.

Alignment

C program linkage:
C programs are read via DLL to achieve external integration.

Applicable equipment

Semiconductor processing equipment
Semiconductor wafer processing equipment
Automobile tire manufacturing equipment
Automotive parts manufacturing equipment
Steel product manufacturing equipment

Case studies

Case 1: Equipment monitoring

Detects equipment abnormalities and visualizes and monitors the machining status. When an abnormality occurs, it is notified to the PLC and SPC/GB monitoring results are automatically output.

Abnormal notification:Immediate notification to PLC when an abnormality is detected
Visualization of machining status: Visualization of machining status
SPC monitoring: Quality stabilization through statistical process monitoring
GB Surveillance:Detecting waveform deviations to predict abnormalities
Automatic Output

Case 2: Predictive quality monitoring

It learns from inspection result data and predicts the quality of processed products. By integrating it with equipment data monitoring results, it is possible to grasp the quality of finished products in advance, stabilizing quality and reducing defects.

Inspection data learning:Build a quality prediction model based on inspection results
Equipment Data Integration:Utilizing the results of waveform monitoring, SPC monitoring, and multivariate monitoring
Predictive Quality Monitoring:Predict the quality of processed products in real time
Prediction result notification:Quality prediction results can be output as an alarm or in CSV format

Example of quality prediction monitoring

System Requirements

Intel Core i7/i9 equivalent PC, workstation, or server
Memory 16GB or more
Storage M.2 SSD (PCI-Express) 1TB or more
Supported OS: Windows 11, Windows Server 2016 / 2019 / 2022 / 2025

TDX performance

Up to 5,000 signals(items) per license（Each column in the CSV file is one signal）
Supports up to 99 types different CSV formats
Maximum CSV file length: 24 hours
Minimum sampling interval: 1ms（CSV file line spacing）
The maximum amount of data read per unit time depends on the PC performance.