edge2ai_pred_maint | Predictive Maintenance at Edge
kandi X-RAY | edge2ai_pred_maint Summary
kandi X-RAY | edge2ai_pred_maint Summary
Predictive Maintenance at the Edge. Traditional computing architecture based predictive analytics engine relies on the model training and serving computations hosted on on-prem, cloud or hybrid infrastructure. The engine is invoked by a remote asset over a network with data on which the engine provides the inferences. This to and fro information exchange between the data generating asset and the predictive engine suffers from latency, potential dependence on network bandwidth and stability. As connected devices proliferate in Industrial assets, there will be use cases of local computation on these devices driven by limited or intermittent network connectivity and need for real-time decision making. These devises at the edge may additionally have specific data processing challenges as driven by constraints in power consumption, compute, memory and storage capabilities.
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edge2ai_pred_maint Examples and Code Snippets
Community Discussions
Trending Discussions on Edge Computing
QUESTION
I was given a task to implement an input validator with the Skooma library https://github.com/bobfp/skooma#validators
The general concept is pretty clear, but for some inputs I have a list of "legal" words, and I have zero clue on how to implement the validation for this case. Hence why I came here, I wanted to ask if you know any examples / projects that used this library? I googled but didn't find anything. Of if you have any other tipps just let me know! 🙂 This is the example:
my schema:
...ANSWER
Answered 2022-Jan-31 at 00:05You need a custom validator function, here's an example:
QUESTION
I am working on edge computing for IoT applications and expected to create a system that acts as a near edge computer with the use of a raspberry pi hooked up to a dht11 sensor. How do I send this data over to a computer that is at the edge? Ideally I want to use my PC as this device but I have no clue how to send this data over in real time.
So far I have created the circuit and can view the temperature and humidity readings on the raspberry pi in python. Unsure of what the next steps are - I don't want to send this data over to the cloud just yet.
Side note: I believe i may be missing knowledge regarding this but is the raspberry pi an edge device because it is hooked up to the sensor directly?
Any help is greatly appreciated.
...ANSWER
Answered 2021-Jan-22 at 10:01You need to think this through a bit more. What will you do with the temperature and humidity data that you receive?
For example, if you're just experimenting and want to just see the readings in a console on your PC, you can use netcat to send the console output of your Python program from the RPi to PC. No SW development needed, they just have to be in the same network. Not particularly useful for anything else, either.
Otherwise you need to set up some client-server solution between the RPi and your PC. There's a ton of possible solutions, all depending on what you plan to do with the data. You can use MQTT, HTTP, a straight database connection (MySQL, PostgreSQL), etc. You have to supply both sides of the connection. The Python code on client side which connects and sends data; and the server side thing that accepts the samples and stores them somewhere. Plus all the networking, authentication etc.
Or you can just download the Python client libraries for your favourite cloud solution and set that up according to a tutorial. TBH, this sounds a lot less work to me.
QUESTION
I don't have a lot of knowledge computing the complexity. Can you help estimate the complexity of the following pseudo-codes?
Algorithm 1:
...ANSWER
Answered 2020-Jun-21 at 11:50The algorithm1 will first perform simple multiplication and addition on vectors. Assuming that it loops from start to end on each vector and performs some calculations, the number of iterations made would be
3*N
which would be consideredO(N)
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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No vulnerabilities reported
Install edge2ai_pred_maint
Create a pull request for this project from github
Launch a new Project Session with default parameters
run the following command by launching Terminal to setup the necessary Python packages
Walk through the Predictive Maintenance.ipynb for injestion, exploration, training and model persistence
Alternatively, you can just train the model directly by executing running hte file model_training.py
Check whether the model has been persisted in model folder.
Once the model is saved, you can check if the model is serving inferences for a sample record by executing the following command
You should see the response as below. The Response interpretation is the Remaining Useful Life (RUL) predicted is 118 Time cycles. The machine is not predicted to Fail. The probability of failure is 12%
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