Our Intelligent Data Infrastructure Addresses Costly Issues with Industrial Production Lines

Currently, there is a lot of hype about AI in industrial production.

Yet, before you think about deploying an AI driven solution, you should first ascertain if you have got an appropriate stack of data.

Our experience, which dates back to 2018, tells us: in most cases this is not the case. As a consequence, AI solutions won’t help cutting cost. To the contrary, they will produce additional cost. PANDA | IDI is a unique kit of software (PANDA | Drift), Hardware (Edge | Device)
and optional algorithms.

lets you Operate with Data in line with both, AI and your Particular Needs

Your machines and sensors should produce meaningful multi-modal data in a homogenous way. Often, you will require a vast amount of high-frequency data to arrive at meaningful conclusions. Yet, your plant might stop working altogether, if those data would jam your factory or machine data networks.

PANDA’s Intelligent Two-Pronged Edge and Network - Based Data Solution Addresses this very Challenge:

It avoids data jams paralyzing your factory while at the same time allowing aggregated data getting stored and algorithms getting deployed on your edge.
Standard Software
Microservices

The Data Flow between Services is Organized by Streams

PANDA | DRIFT uses a MQTT Broker to connect different services and data flows with an efficient and flexible pub-sub system.

How does PANDA | DRIFT Work?

Each service is configured with a JSON file and a docker compose file connects the services. Add custom code with templates.
Choose a microservices and configure it with a JSON files.
BUILD
Write a docker compose file to route the services.
RUN
Build & deploy a docker image to a device.
Write a JSON file to configure a service

version: '3'
services:

  mqtt-broker:
    image: registry.panda.technology/drift/devops/mqtt_broker_amd64
    ports:
      - "1883:1883"

  trigger-service:
    image: registry.panda.technology/drift/system_services/trigger_service:dev
    command: python3 -m trigger_service
    environment:
      MQTT_BROKER_URL: tcp://mqtt-broker:1883
      CONFIGURATION_PATH: /var/config/trigger-service.json
      LOG_LEVEL: INFO
    volumes:
      - ./config:/var/config
    depends_on:
      - mqtt-broker

  hello-service:
    build: .
    ports:
      - "8000:8000"

version: '3.8'
services:
  mqtt-broker:
    image: registry.panda.technology/drift/devops/mqtt_broker_amd64
    ports:
      - "1883:1883"

  trigger-service:
    image: registry.panda.technology/drift/system_services/trigger_service:dev
    command: python3 -m trigger_service
    environment:
      CONFIGURATION_PATH: /var/config/trigger_service.json
      MQTT_BROKER_URL: tcp://mqtt-broker:1883
    depends_on:
      - mqtt-broker
    volumes:
      - ./config:/var/config/

  input-proxy-service:
    image: registry.panda.technology/drift/system_services/input_proxy_service:dev
    command: drift_input_proxy_service
    volumes:
      - ./config:/var/config
      - /dev:/dev
    ipc: host
    environment:
      MQTT_BROKER_URL: tcp://mqtt-broker:1883
      CONFIGURATION_PATH: /var/config/input_proxy_service.json
      MQTT_CLIENT_ID: drift_input_proxy_service
      OUTPUT_MQTT_TOPIC: camera/compressed
      INPUT_MQTT_TOPIC: camera_image_notify_1
      LOG_LEVEL: DEBUG
    depends_on:
      - mqtt-broker

  daheng:
    image: registry.panda.technology/drift/io_services/shmemory_camera_service:dev-daheng
    volumes:
      - ./config:/var/config
      - /dev:/dev
    ipc: host
    privileged: true
    environment:
      CONFIGURATION_PATH: /var/config/daheng.json
      MQTT_BROKER_URL: tcp://mqtt-broker:1883
      MQTT_CLIENT_ID: shared_memory_camera_service
      TRIGGER_INPUT_MQTT_TOPIC: trigger
      TRIGGER_OUTPUT_MQTT_TOPIC: camera_image_notify
      SHARED_MEMORY_BASE_NAME: shared_image
      SHARED_MUTEX_BASE_NAME: shared_mutex
    depends_on:
      - trigger-service

  data-exporer:
    image: registry.panda.technology/drift/data_explorer/data_explorer_backend:dev
    depends_on:
      - mqtt-broker
    environment:
      MQTT_BROKER_URL: tcp://mqtt-broker:1883
      MQTT_CLIENT_ID: data_explorer_backend-1
    ports:
      - "8000:8000"

version: '3'
services:

  mqtt-broker:
    image: registry.panda.technology/drift/devops/mqtt_broker_amd64
    ports:
      - "1883:1883"

  trigger-service:
    image: registry.panda.technology/drift/system_services/trigger_service:dev
    command: python3 -m trigger_service
    environment:
      MQTT_BROKER_URL: tcp://mqtt-broker:1883
      CONFIGURATION_PATH: /var/config/trigger-service.json
      LOG_LEVEL: INFO
    volumes:
      - ./config:/var/config
    depends_on:
      - mqtt-broker

  opcua-client:
    image: registry.panda.technology/drift/io_services/opc-ua-client-service:v1.1.2
    restart: always
    environment:
      MQTT_BROKER_URL: tcp://mqtt-broker:1883
      MQTT_CLIENT_ID: opcua-client
      OUTPUT_MQTT_TOPIC: opcua-data
      CONFIGURATION_PATH: /var/config/opcua.json
      LOG_LEVEL: INFO
    volumes:
      - config:/var/config/

  influx-writer:
    image: registry.panda.technology/drift/system_services/influx_writer:dev
    volumes:
      - ./config:/var/config
    environment:
      MQTT_BROKER_URL: tcp://mqtt-broker:1883
      MQTT_CLIENT_ID: influx-writer
      CONFIGURATION_PATH: /var/config/influx-writer.json
      INFLUX_URL: http://influxdb:8086
      INFLUX_ORG: ferrum
      INFLUX_TOKEN: ${INFLUX_TOKEN}
    depends_on:
      - mqtt-broker
      - influxdb


  influxdb:
    image: influxdb:2.0-alpine
    environment:
      DOCKER_INFLUXDB_INIT_MODE: setup
      DOCKER_INFLUXDB_INIT_BUCKET: data
    volumes:
      - "./influxdb:/var/lib/influxdb2"
    healthcheck:
      test: [ "CMD", "influx", "-version" ]
    ports:
      - "8086:8086"


Write a docker compose file to connect the services.

{
  "endpoint_url": "opc.tcp://127.0.0.1:4840",
  "subscription_parameters": {
    "publishing_interval": 500,
    "monitoring_sample_rate": 100
  },
  "buffer_size": 100,
  "authentication": {
    "type": "anonymous"
  },
  "security": {
    "mode": "none",
    "policy": "None"
  },
  "variables": [
    "ns=1;s=sinusoid_noise",
    "ns=1;s=sinusoid"
  ]
}

{
  "triggering": {
    "capture_rate_ms": 1000
   },
   "triggers":[
      "SIMPLE_TRIGGER",
      "INTERVAL_TRIGGER"
   ]
}

{
  "type": "DAHENG",
  "capture_timeout_ms": 2000,
  "camera": {
    "settings": [
      {
        "enable": true,
        "instances": 2,
        "uid": "NE0200070087",
        "trigger_source": "software_trigger",
        "exposure_auto": "continuous",
        "gain_auto": "continuous",
        "balance_ratio_auto": "continuous"
      }
    ]
  }
}

Bundles a Best Selection of Open Source Software and ML Libraries.

ca. 4 entries

Continuous Integration and Deployment for DevOps

Keep overview and order with continuous integration and deployment. Monitor your decentralized solution with dashboarding tools.

Use PANDA | DRIFT for on-Premise Applications, no Cloud Needed

From data acquisition to fully automated evaluation and pattern recognition with the help of artificial intelligence: Build your own independed industrial AI platform embedded in the control cabinet.
Sensors
Embedded devices
Touch panels

Install PANDA | DRIFT on an Edge Device and Get Immediately Started with AI

PANDA | DRIFT offers a complete solution to handle and manage Linux based edge devices. Algorithms, Drivers and Databases, you get everything in a small setup on industrial hardware.
Interface Carrier
  • Camera & 4 sensor ports
  • 14.0 interfaces (WLAN/LAN)
  • 1TB SSD mass storage
Multimode Amplifyer Board
  • Amplification
  • Filtering
  • A-D Convesrion
Compute Board
  • 8-Core CPU
  • 8 TOPS TPU Co-Processor
  • 32GB RAM

Start with PANDA's Preconfigured Reference Hardware

PANDA provides a portfolio of highly optimized hardware for DRIFT
USB Power Banks
Plug & Play
4 channel
48.000 Hz
A/D-Converter
ADS8866 Texas
16 Bit
The basis for the application of AI methods is excellent data quality. Industrial sensors of any kind can be processed directly on PANDA’s edge devices by using PANDAs sensor extensions.
USB Power Banks
Plug & Play
4 channel
48.000 Hz
A/D-Converter
ADS8866 Texas
16 Bit
The basis for the application of AI methods is excellent data quality. Any industrial sensors can be processed directly on edge devices by using PANDAs sensor extensions.
USB Power Banks
Plug & Play
4 channel
48.000 Hz
A/D-Converter
ADS8866 Texas
16 Bit
The basis for the application of AI methods is excellent data quality. Any indutrial sensors can be processe directly in edge devices by using PANDAs sensor extensions.

The Digital Transformation of your Production Starts with Data Quality

To ensure that all information about the machine condition and the product quality can be recorded easily, DRIFT offers streaming interfaces for sensors. Your equipment can be retrofit with sensors, without modifying the existing machine control system.
Vibrationssensor
Messprinzip:
Piezo mit Schwungmasse
Applikation:
Konkakt
Messbereich:
1 Hz bis 10000 Hz
Genauigkeit:
±5 Hz
Wiederholbarkeit:
1%
Sonstiges:
Querrichtungsempfindlichkeit <10%
PT 100 Sensor
Messprinzip:
Platinmesswiderstand
Applikation:
Kontakt
Messbereich:
'-40°C bis 250°C
Genauigkeit:
±1°C
Wiederholbarkeit:
1%
Sonstiges:
-
IR Temperatursensor
Messprinzip:
Infrarotsensor
Applikation:
Kontaktlos
Messbereich:
'10°C bis 250°C
Genauigkeit:
±1,5%
Wiederholbarkeit:
1%
Sonstiges:
-
Abstandssensor
Messprinzip:
Laserreflektion auf lineares Array
Applikation:
Kontaktlos
Messbereich:
20 mm bis 300 mm
Genauigkeit:
±10 µm
Wiederholbarkeit:
1%
Sonstiges:
-
VIS Spektrometer
Messprinzip:
Lichtstreuung im sichtbaren Spektrum
Applikation:
Kontaktlos
Messbereich:
405nm bis 1500nm
Genauigkeit:
± 15nm
Wiederholbarkeit:
10%
Sonstiges:
-
Stromsensor
Messprinzip:
Hall-Effekt
Applikation:
Kontaktlos
Messbereich:
0 A bis 500 A
Genauigkeit:
± 5 A
Wiederholbarkeit:
5%
Sonstiges:
-
Durchflusssensor
Messprinzip:
Schaufelrad
Applikation:
Kontakt
Messbereich:
2 l/min bis 100 l/min
Genauigkeit:
±5 l/min
Wiederholbarkeit:
5%
Sonstiges:
Druckbereich: -50 kPa bis 500 kPa Anschlüsse: Swage, 8 mm, 5/16
NIR Spektrometer
Messprinzip:
Lichtsteuerung im kurzwelligen Infrarotbereich
Applikation:
Kontaktlos
Messbereich:
900 nm bis 1700 nm
Genauigkeit:
±12 nm
Wiederholbarkeit:
2%
Sonstiges:
-
Richtmikrofon
Messprinzip:
Elektretkondensator
Applikation:
Kontaktlos
Messbereich:
20 Hz bis 20 KHz
Genauigkeit:
20 Hz
Wiederholbarkeit:
2%
Sonstiges:
14 mV / Pa Sensitivität
Drucksensor
Messprinzip:
Verformung eines Piezokristalls
Applikation:
Kontakt
Messbereich:
0 bar bis 20 bar
Genauigkeit:
0,2 bar
Wiederholbarkeit:
2%
Sonstiges:
Temperaturbereich -20°C bis 85°C
Optische Kamera
Messprinzip:
CMOS-Photosensor
Applikation:
Kontaktlos
Messbereich:
2,3 Mpixel
Genauigkeit:
-
Wiederholbarkeit:
-
Sonstiges:
164,5 fps
Digitalmuxer
Messprinzip:
Digital- auf Analogmuxer
Applikation:
Kontakt
Messbereich:
0V bis 30V Digitalsignal
Genauigkeit:
±0,1 V
Wiederholbarkeit:
1 kHz Abtastrate
Sonstiges:
16 Digitalkanäle pro Analogkanal
Partikelsensor in Flüssigkeiten
Messprinzip:
-
Applikation:
-
Messbereich:
-
Genauigkeit:
-
Wiederholbarkeit:
-
Sonstiges:
-
Thermische Kamera
Messprinzip:
2D-Array für
Applikation:
Infrarotspektrum
Messbereich:
Kontaktlos
Genauigkeit:
-20°C bis 650°C
Wiederholbarkeit:
±0,06°C
Sonstiges:
2% Auflösung: 320 x 240 Pixel
Umweltsensor
Messprinzip:
Widerstandsmessung an Metalloxid
Applikation:
Kontaktlos
Messbereich:
0 IAQ bis 500 IAQ
Genauigkeit:
1 IAQ
Wiederholbarkeit:
15%
Sonstiges:
Gleichzeitige Temperatur - und Luftfeuchtemessung
Farbsensor
Messprinzip:
IR Fotodiode
Applikation:
Kontaktlos
Messbereich:
350 nm bis 1000 nm
Genauigkeit:
30 nm
Wiederholbarkeit:
3%
Sonstiges:
-
Kraftmessdose
Messprinzip:
Dehnungsmessstreifen-Brückenschaltung
Applikation:
Konkakt
Messbereich:
-20 kN bis 20 kN
Genauigkeit:
±20 N
Wiederholbarkeit:
0,1
Sonstiges:
-
Viskositätssensor
Messprinzip:
Akustische Fluidmessung
Applikation:
Kontakt
Messbereich:
0,1 Pa • s bis 10 Pa • s
Genauigkeit:
0,5 Pa • s
Wiederholbarkeit:
1%
Sonstiges:
-

Use PANDA | DRIFT to Synchronize and Store Data from Different Sources

One of the most striking features of DRIFT is the ability to sync different data sources that contain high or low frequency information or even images.
Real Time
Async Time
Stream buffers
Real Time
Cameras
Machine Data
Synchronization
Storage

AI Ready Data Aquisition with PANDA | DRIFT

You Want to Deploy your own Algorithm?

PANDA | DRIFT is 100% compatible with Python and ML libraries

Use Preconfigured AI Services

PANDA | DRIFT provides starting points for common use cases
Computer Vision (simple application 1 - one day to make it work)
  • 1 camera
  • 1 DNN algorithm for object detection
  • 1 Custom code to use bounding boxes to black out workers
  • Inline - visualization app

Agile AI Development Made Easy

Start developing with your favorite tools, then seamlessly switch to high efficient ML-implementations by utilizing DRIFT's streaming format.
apply algorithm on full resolution with standard tools
internal storing and streaming
apply optimized algorithm in high performance low latency mode directly on compressed data

Process Large Data Efficiently with PANDA | DRIFT’s Wavelet Technology

DRIFT’s internal streaming data is based on wavelet decompositions. It enables a universal treatment of matrix - like data formats by utilizing a Bfloat16 sparse matrix representation.
20x
Less storage space for data
10x
Speed-up for ML algorithms

WBS data format for time series

decompose
80% of values
are zeros
80% of values
are zeros
80% of values
are zeros
80% of values
are zeros
80% of values
are zeros
80% of values
are zeros
80% of values
are zeros
80% of values
are zeros
compose
90% of values = zeros

WBS data format for images

decompose
compose

DRIFT’s WBS data format compared to alternative ML formats

in RAM (floats, random access, direct access for algorithms and linear algebra operations)
[npy]
[npy]
on SSD or Network-traffic
(binary, no direct access for algorithms)
.npy
.jpg
.wb
kBytes (less is better)

Learn More about Solutions that are Built with PANDA | DRIFT

Automated Quality inspection

Computer Vision building blocks

The perfect start for AI in production. A computer vision system is a camera-based quality control system via pattern recognition and without a cloud system. Industrial interfaces for automation include OPC-UA or Profinet.

Condition Monitoring

Anomaly detection and life time prediction

Empower the maintenance team with modern pattern recognition methods to indentify problems before they oocure. Use DRIFT to build your contition monitoring and predictive maintenance platform.

Dependency Mining

Learn how to use Metric Spaces to find dependencies in any data format

A mathematical framework developed specifically for applications in industrial production that is based on topolocial theory.

Documentation

Check the full documenation of DRIFT microservices

Learn how to build your data-driven AI platform using DRIFT microservices and connected open source software.

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