predictive-maintenance | Demonstration of MapR for Industrial IoT | Dashboard library

by mapr-demos Jupyter Notebook Version: Current License: Apache-2.0

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | predictive-maintenance Summary

predictive-maintenance is a Jupyter Notebook library typically used in Analytics, Dashboard, Grafana applications. predictive-maintenance has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

There are two objectives relating to predictive maintenance implemented in this project. The first objective is to visualize time-series data in an interactive real-time dashboard in Grafana. The second objective is to make raw data streams and derived features available to machine learning frameworks, such as Tensorflow, in order to develop algorithms for anomaly detection and predictive maintenance. These two objects are realized using two seperate data flows:. Put together, these data pipelines look like this. The APIs used for reading and writing data are shown in red.

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Quality

Security

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Support

predictive-maintenance has a low active ecosystem.

It has 73 star(s) with 45 fork(s). There are 19 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of predictive-maintenance is current.

Quality

predictive-maintenance has 0 bugs and 0 code smells.

Security

predictive-maintenance has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

predictive-maintenance code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

predictive-maintenance is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

predictive-maintenance releases are not available. You will need to build from source code and install.

Installation instructions are not available. Examples and code snippets are available.

It has 910 lines of code, 7 functions and 6 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

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predictive-maintenance Key Features

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predictive-maintenance Examples and Code Snippets

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Community Discussions

Trending Discussions on predictive-maintenance

define a failure horizon based on serial number and failure label

Import error while Executing AWS Predictive Maintenance Using Machine Learning Sample

QUESTION

define a failure horizon based on serial number and failure label

Asked 2021-Nov-22 at 17:30

I wanted to re-label the healthy label (0) to failure label (1) 3 days before the actual failure (1) like what they did in the attached link: Reference link. It worked well for the equal time length but did not work for the variable length. That is, all serial number must fail in the same day which doesn't make sense. For the sample dataset, we see that serial C failed in January 5, 2014, A failed in January 6, and A failed in January 7. I want to relabel the re-label the healthy label (0) to failure label (1) 3 days before the actual failure (1) for the serial number C, and for the other serial numbers as well. I appreciate your time. Thanks!

My code:

...

ANSWER

Answered 2021-Nov-22 at 17:30

Here is a working solution. Note: I preferred to rewrite it completely for clarity.

Source https://stackoverflow.com/questions/69995086

QUESTION

Import error while Executing AWS Predictive Maintenance Using Machine Learning Sample

Asked 2020-Apr-28 at 08:50

We are trying to execute and check what kind of output is provided by Predictive Maintenance Using Machine Learning on AWS sample data. We are referring Predictive Maintenance Using Machine Learning and AWS Guide to launch the sample template provided by the AWS. The template is executed properly and we can see the resources in account. Whenever we run the sagemaker notebook for the given example we are getting the error in CloudWatch logs as follows

...

ANSWER

Answered 2020-Apr-28 at 08:50

A fix for this issue is being deployed to the official solution. In the meantime, you can make the changes described here in your SageMaker environment by following the instructions below:

1) In the notebook, please change the framework_version to 1.6.0.

Source https://stackoverflow.com/questions/61389632

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install predictive-maintenance

You can download it from GitHub.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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