Remote Data Acquisition

Remote data acquisition and analysis platform


Remote data acquisition solution for automobiles

Remote Data Acquisition

Remote data acquisition and analysis platform

Every process such as development, validation, production, and maintenance is digitized in the machine industry including automobiles, heavy equipment, construction equipment, farm equipment, and robots. Data acquisition, which is the main requirement in these areas, requires building an advanced data pipeline that can handle sensor data with high sampling rates and diverse fusion data. By collecting this high-definition mixed digital data from a remote location without human intervention through a public network such as a mobile network or the Internet, you can leverage data for numerical analysis, simulation, AI development, or other purposes more rapidly. In this way, remote data acquisition enabled by intdash assumes a very important role in industrial digitization.

Use cases

Automotive development: Acquisition of validation data in the V-shaped process and feedback to the development process

You can remotely collect vehicle performance data and build a data pipeline in the cloud. This allows you to manage data more efficiently and share data with other processes such as simulation.

Automotive development: Fusion data measurement in a test course

Measurement data such as CAN (Controller Area Network) data and various sensor signals is transmitted in real-time from the vehicle to the cloud at shakedown in the test course in the course of development. Measurement data can be rapidly distributed to multiple development sites.

Development of heavy equipment, construction equipment, or farm equipment: Simultaneous measurement of multiple machines

Real-time transmission of mixed data such as machine data (J1939), GPS, and video from each machine to the cloud enables comparison analysis and real-time monitoring of each machine in a test field or test site.

Automotive development: Remote data acquisition and visualization in an overseas test drive

You can instantaneously share driving data from an overseas development site for product validation by transmitting it to the global cloud in real-time.

Processing and numerical analysis of collected data

Primary processing such as conversion of massive measurement data into physical values, resampling, and filtering is performed with the computing power of the cloud. You can build a data pipeline to seamlessly communicate data to analysis tools or external systems for secondary processing.

Challenges related to remote data acquisition

Remote data acquisition from industrial instruments to collect a large amount of time-series data brings to light many challenges in terms of IT (Information Technology) and OT (Operational Technology).

  • Support for the data formats, interfaces, and protocols specific to the machine equipment
  • Transmission of several tens of thousands of time-series data generated per second
  • Server performance that can handle massive data rates and simultaneous access from many devices
  • Different clocks among different devices and cumbersome time setting
  • Network disconnection in an unstable network and data loss due to network disconnection

Main features of DX Functions

Hardware to solve OT challenges

aptpod provides terminal appliances equipped with a wide range of interfaces and provides peripheral devices to acquire many different control signals.

View Details

Support for fusion data

Many different data types such as various sensor signals, industry-specific fieldbus signals, video, and audio are supported.

View Details

Automatic loss collection processing

Data lost during remote data collection using an unstable network such as a mobile network or wireless LAN are automatically resent.

View Details

Integrated timestamp management

Timestamps are added to data acquired from different kinds of devices and the data is mapped to the same time axis for management.

View Details

Scalable high-speed load distribution with high performance

Proprietary high-performance sharding mechanism with linear scalability that can store massive amounts of time-series data at high speed (distributed management of time-series data)

View Details

Tool suite for data measurement

A variety of tool application suites help to manage measurement targets and edges, filter edge data, monitor and check the measurement status, and browse acquired data.

View Details

Advanced visualization

Web dashboard to visualize every type of time-series data in real-time on demand

View Details