Virtual exhibition:- https://us-east-1.sumerian.aws/b0bbeaf5a3904d1f98a416864e598101.scene
--Courtsey of Psitron Technologies
https://github.com/awslabs/predictive-maintenance-using-machine-learning/tree/master/source/notebooks
https://github.com/tensorflow/tensorflow/blob/r1.8/tensorflow/examples/get_started/regression/custom_regression.py
https://www.hackster.io/psitron/personal-protective-body-camera-ppbc-9fc395 https://aws.amazon.com/blogs/iot/using-aws-iot-for-predictive-maintenance/
https://youtu.be/_TVKa-noVKE https://cloud.google.com/blog/products/data-analytics/a-process-for-implementing-industrial-predictive-maintenance-part-ii https://docs.aws.amazon.com/sagemaker/latest/dg/cdf-training.html https://www.kaggle.com/nafisur/predictive-maintenance-using-lstm-on-sensor-data https://www.mdpi.com/2227-7390/6/11/242/pdf
https://www.kaggle.com/learn/data-cleaning https://aws.amazon.com/blogs/machine-learning/now-available-in-amazon-sagemaker-deepar-algorithm-for-more-accurate-time-series-forecasting/ https://www.infoq.com/articles/machine-learning-techniques-predictive-maintenance/ https://aws.amazon.com/blogs/aws/amazon-sagemaker-neo-train-your-machine-learning-models-once-run-them-anywhere/
https://software-dl.ti.com/processor-sdk-linux/esd/docs/latest/linux/Examples_and_Demos/Application_Demos/PdM_Anomaly_Detection.html pm for dlr https://aws.amazon.com/releasenotes/sagemaker-neo-supported-frameworks-and-operators/ neo supported format list https://docs.aws.amazon.com/sagemaker/latest/dg/neo-troubleshooting.html neo error list
https://aws.amazon.com/blogs/machine-learning/aws-launches-open-source-neo-ai-project-to-accelerate-ml-deployments-on-edge-devices/ https://docs.aws.amazon.com/greengrass/latest/developerguide/ml-inference.html https://aws.amazon.com/blogs/machine-learning/save-on-inference-costs-by-using-amazon-sagemaker-multi-model-endpoints/
https://www.google.com/amp/s/limblecmms.com/blog/predictive-maintenance/%3famp https://youtu.be/RmVWKLbLq2Y https://youtu.be/cfbKR48nSyQ https://www.youtube.com/redirect?redir_token=QUFFLUhqbWt0Z29HS3NGcWxNQ3ZGM3I2bGU2eXZzeFJMQXxBQ3Jtc0ttN1ZZR3AzalVUZnBPU3FUX1BUaS1EeWc2SjBrNkZyUFp6dHR6R1dFc3lvUnBYZU52REZ4QW44bUFfMEZ2WEY2TVI1ejZnSUpwQzdLWU5kT1o0T1RUdm8wYzBscEo1bzhsamY5NThLYzJGMnoxaldxZw%3D%3D&q=http%3A%2F%2Fbit.ly%2F2te1awP&event=video_description&v=Dd_4rbWYgI4
https://in.mathworks.com/help/predmaint/index.html?s_tid=CRUX_lftnav https://classeval.wordpress.com/introduction/basic-evaluation-measures/#:~:text=P%20%2B%20N).-,Accuracy,be%20calculated%20by%201%20%E2%80%93%20ERR http://systems-sciences.uni-graz.at/etextbook/bigdata/confusionmatrix.html
http://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/predictive-maintenance-playbook#business-case-for-predictive-maintenance
https://www.infopulse.com/blog/anomaly-detection-solutions-for-predictive-maintenance-of-industrial-equipment-and-systems/#:~:text=With%20the%20help%20of%20real,merely%20the%20onset%20of%20it https://www.crunchmetrics.ai/blog/Is-anomaly-detection-supervised-or-un-supervised/ https://github.com/ashishpatel26/Predictive_Maintenance_using_Machine-Learning_Microsoft_Casestudy
https://github.com/ashishpatel26/Predictive_Maintenance_using_Machine-Learning_Microsoft_Casestudy https://ftmaintenance.com/maintenance-management/using-root-cause-analysis-to-improve-maintenance/ https://aithority.com/guest-authors/how-to-find-the-right-machine-learning-techniques-for-predictive-maintenance/
https://notebooks.azure.com/Microsoft/projects/PredictiveMaintenance/html/Predictive%20Maintenance%20Modeling%20Guide%20Python%203%20Notebook.ipynb https://towardsdatascience.com/multi-class-metrics-made-simple-part-ii-the-f1-score-ebe8b2c2ca1 https://github.com/NeelanjanG/AWS-Sgmkr-NBs https://www.onupkeep.com/answers/predictive-maintenance/incorporate-predictive-maintenance-without-sensors/#:~:text=There%20are%20two%20possible%20ways,failures%20(low%2Dtech)
https://arun-thomas.xyz/2019-06-18-BearingAnalytics/#:~:text=Rather%20than%20shutting%20down%20equipment,detect%20faults%20and%20predict%20failure https://limblecmms.com/blog/a-maintenance-managers-guide-to-reliability-centered-maintenance/ https://in.mathworks.com/help/stats/classification.html https://www.svds.com/getting-started-predictive-maintenance-models/ https://www.kaggle.com/billstuart/predictive-maintenance-ml-iiot/notebook
https://in.mathworks.com/help/predmaint/ug/similarity-based-remaining-useful-life-estimation.html https://blog.bosch-si.com/industry40/industry-4-0-10-use-cases-for-software-in-connected-manufacturing/ https://blog.bosch-si.com/industry40/industry-4-0-predictive-maintenance-use-cases-in-detail/
https://aws.amazon.com/blogs/aws/amazon-sagemaker-neo-train-your-machine-learning-models-once-run-them-anywhere/ https://docs.aws.amazon.com/cli/latest/reference/sagemaker/create-compilation-job.html https://blog.bigml.com/2016/09/28/logistic-regression-versus-decision-trees/#:~:text=Decision%20Trees%20bisect%20the%20space,the%20space%20exactly%20into%20two.&text=A%20single%20linear%20boundary%20can%20sometimes%20be%20limiting%20for%20Logistic%20Regression https://turi.com/learn/userguide/supervised-learning/boosted_trees_classifier.html
https://mxnet.apache.org/versions/1.7/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html
https://github.com/neo-ai/neo-ai-dlr/tree/master/sagemaker-neo-notebooks/edge https://github.com/awsdocs/aws-greengrass-developer-guide/blob/main/doc_source/ml-console.md https://github.com/awsdocs/aws-greengrass-developer-guide/blob/main/doc_source/ml-dlc-console.md https://github.com/awsdocs/aws-greengrass-developer-guide/blob/main/doc_source/what-is-gg.md#gg-ml-samples
https://mxnet.apache.org/versions/1.7/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html https://sagemaker.readthedocs.io/en/stable/frameworks/mxnet/using_mxnet.html https://mxnet.apache.org/versions/1.0.0/api/python/gluon/gluon.html#mxnet.gluon.HybridBlock https://mxnet.apache.org/versions/1.7/api/python/docs/_modules/index.html
https://mxnet.apache.org/versions/1.0.0/_modules/mxnet/gluon/block.html#HybridBlock.export https://gluon-ts.mxnet.io/examples/synthetic_data_generation_tutorial/tutorial.html https://mxnet.apache.org/versions/1.4.1/tutorials/gluon/hybrid.html
https://discuss.mxnet.apache.org/t/save-cnn-model-architecture-and-params/683/2 http://mxnet.incubator.apache.org/versions/0.11.0/tutorials/basic/module.html
https://github.com/dmlc/xgboost/blob/master/doc/parameter.rst#id3 https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost_hyperparameters.html
https://github.com/aws/sagemaker-python-sdk/tree/v1.12.0/src/sagemaker/tensorflow#tensorflow-sagemaker-estimators-and-models
https://github.com/awslabs/amazon-sagemaker-examples/blob/master/sagemaker_neo_compilation_jobs/tensorflow_distributed_mnist/mnist.py
https://github.com/aws/sagemaker-python-sdk/tree/v1.12.0/src/sagemaker/tensorflow#tensorflow-sagemaker-estimators-and-models
https://www.tensorflow.org/api_docs/python/tf/strings/as_string https://www.tensorflow.org/api_docs/python/tf/estimator/DNNClassifier
https://github.com/awslabs/amazon-sagemaker-examples/blob/master/sagemaker_neo_compilation_jobs/tensorflow_distributed_mnist/mnist.py
https://github.com/aws/sagemaker-python-sdk#tensorflow-sagemaker-estimators https://docs.aws.amazon.com/sagemaker/latest/dg/neo-troubleshooting.html https://www.tensorflow.org/tutorials/load_data/tfrecord
https://www.tensorflow.org/tutorials/load_data/tfrecord https://docs.aws.amazon.com/sagemaker/latest/dg/neo-troubleshooting.html
https://github.com/awslabs/amazon-sagemaker-examples/blob/master/hyperparameter_tuning/keras_bring_your_own/hpo_bring_your_own_keras_container.ipynb
neo-ai/neo-ai-dlr#24 https://github.com/awsdocs/amazon-sagemaker-developer-guide/blob/master/doc_source/neo-troubleshooting.md
https://github.com/aws/sagemaker-python-sdk/tree/v1.15.0/src/sagemaker/tensorflow https://docs.aws.amazon.com/dlami/latest/devguide/onnx.html
https://github.com/awslabs/amazon-sagemaker-examples/tree/master/sagemaker-python-sdk/tensorflow_script_mode_training_and_serving
https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/model.py https://www.tensorflow.org/lite/guide/ops_custom tensorflow/tensorflow#11674 tensorflow/tensorflow#21617 https://docs.aws.amazon.com/sagemaker/latest/dg/tf.html
https://machinelearningmastery.com/xgboost-for-time-series-forecasting/#:~:text=XGBoost%20is%20an%20implementation%20of,model%20for%20time%20series%20forecasting
https://github.com/mayurm23/Big-Data-Project/blob/master/Predictive%20Maintenance%20XGBoost.ipynb https://stackoverflow.com/questions/56873573/sagemaker-neo-custom-model-fn-and-predict-fn
https://github.com/awsdocs/amazon-sagemaker-developer-guide/blob/master/doc_source/neo-deployment-hosting-services-cli.md https://github.com/awsdocs/amazon-sagemaker-developer-guide/blob/master/doc_source/neo.md https://www.geeksforgeeks.org/writing-data-from-a-python-list-to-csv-row-wise/
https://www.tensorflow.org/tutorials/load_data/tfrecord https://www.programcreek.com/python/example/90550/tensorflow.decode_raw https://www.commonlounge.com/discussion/a50fc7fd60c7446ca6aada1b2c8060b8
https://www.tensorflow.org/guide/eager https://neo-ai-dlr.readthedocs.io/en/latest/dev/classdlr_1_1_d_l_r_model.html
https://www.tensorflow.org/tutorials/keras/regression
https://www.tensorflow.org/tutorials/estimator/boosted_trees_model_understanding https://github.com/scikit-learn/scikit-learn/blob/0fb307bf3/sklearn/ensemble/_gb.py#L771 https://towardsdatascience.com/building-a-decision-tree-in-tensorflow-742438cb483e
https://github.com/aqibsaeed/Estimation-of-Remaining-Useful-Life-using-CNN/blob/master/Estimation%20of%20RUL%20using%20CNN.ipynb https://aws.amazon.com/blogs/machine-learning/deploy-trained-keras-or-tensorflow-models-using-amazon-sagemaker/ https://www.pluralsight.com/guides/build-your-first-deep-learning-solution-with-aws-sagemaker
https://www.tensorflow.org/tutorials/keras/save_and_load https://github.com/suriyadeepan/rnn-from-scratch https://github.com/LahiruJayasinghe/RUL-Net
https://medium.com/machine-learning-algorithms/build-basic-rnn-cell-with-static-rnn-707f41d31ee1 https://towardsdatascience.com/extreme-rare-event-classification-remaining-useful-life-estimation-using-lstm-in-keras-81c9b5aa15f0
https://docs.google.com/presentation/d/1YPWKJQMP3dRdIcxcGblTacTFRH1STLdBR6aSMfq0KO4/edit?usp=sharing https://docs.aws.amazon.com/sagemaker/latest/dg/neo-requests-sdk.html
https://youtu.be/1rLxPOxVJoQ https://youtu.be/NOdXRAFEvDo https://aws.amazon.com/blogs/iot/perform-protocol-conversion-at-the-edge-with-aws-lambda-and-aws-greengrass/#:~:text=AWS%20Greengrass%20supports%20OPC%2DUA,based%20on%20rules%20you%20define https://aws.amazon.com/blogs/iot/converting-industrial-protocols-with-aws-iot-greengrass/
https://workshop.industrial-architecture.cloud/setting-up-the-edge-device.html https://docs.aws.amazon.com/greengrass/latest/developerguide/ml-inference.html https://www.google.com/search?q=opc+ua+client+with+greengrass&rlz=1C1SQJL_enIN916IN916&oq=opc+ua+client+with+gree&aqs=chrome.1.69i57j33.16398j0j7&sourceid=chrome&ie=UTF-8 https://github.com/aws-samples/aws-iot-greengrass-opcua-adapter
https://docs.aws.amazon.com/greengrass/latest/developerguide/ml-inference.html https://github.com/aws-samples/amazon-sagemaker-predictive-maintenance-deployed-at-edge https://github.com/forresti/SqueezeNet https://github.com/aws-samples/aws-greengrass-samples https://docs.aws.amazon.com/greengrass/latest/developerguide/ml-console.html#install-mxnet https://docs.aws.amazon.com/greengrass/latest/developerguide/ml-console.html#config-raspberry-pi
https://medium.com/@GalarnykMichael/aws-ec2-part-1-creating-ec2-instance-9d7f8368f78a https://www.chiark.greenend.org.uk/~sgtatham/putty/latest.html https://www.ssh.com/ssh/putty/mac/ http://ec2-54-144-130-64.compute-1.amazonaws.com/
https://www.linkedin.com/posts/ranjith-kumar-guruswamy_opcua-iiot-addvalue-activity-6707150528423493632-AzLv https://github.com/aws-samples/aws-iot-greengrass-opcua-adapter http://node-opcua.github.io/ http://ec2-54-152-121-126.compute-1.amazonaws.com/
https://docs.aws.amazon.com/greengrass/latest/developerguide/quick-start.html https://docs.aws.amazon.com/greengrass/latest/developerguide/quick-start.html#gg-device-setup-troubleshooting https://docs.aws.amazon.com/greengrass/latest/developerguide/device-auth.html#x509-certificates https://docs.aws.amazon.com/greengrass/latest/developerguide/device-auth.html#iot-policies
https://www.raspberrypi.org/documentation/configuration/wireless/desktop.md https://www.raspberrypi.org/documentation/configuration/wireless/wireless-cli.md https://docs.aws.amazon.com/greengrass/latest/developerguide/test-comms.html
https://github.com/aws/aws-iot-device-sdk-python/tree/master/samples/basicPubSub https://docs.aws.amazon.com/greengrass/latest/developerguide/gg-device-start.html
https://aws.amazon.com/blogs/iot/collecting-organizing-monitoring-and-analyzing-industrial-data-at-scale-using-aws-iot-sitewise-part-1/ https://www.kepware.com/en-us/products/kepserverex/ https://aws.amazon.com/blogs/iot/collecting-organizing-monitoring-and-analyzing-industrial-data-at-scale-using-aws-iot-sitewise-part-2/
https://aws.amazon.com/blogs/iot/collecting-organizing-monitoring-and-analyzing-industrial-data-at-scale-using-aws-iot-sitewise-part-3/ https://github.com/open62541/open62541/wiki/List-of-Open-Source-OPC-UA-Implementations https://docs.inductiveautomation.com/display/DOC80/OPC+UA https://www.youtube.com/watch?v=I9RzuJDgbBc
https://docs.aws.amazon.com/greengrass/latest/developerguide/ml-console.html#install-mxnet https://docs.aws.amazon.com/greengrass/latest/developerguide/ml-console.html#package-ml-model https://docs.aws.amazon.com/greengrass/latest/developerguide/gg-core.html#write-directory https://docs.aws.amazon.com/greengrass/latest/developerguide/lambda-functions.html https://aws.amazon.com/blogs/iot/how-to-install-a-face-recognition-model-at-the-edge-device-with-aws-iot-greengrass/
Install Wheel
sudo pip install tensorflow-1.0.1-cp34-cp34m-linux_armv7l.whl
sudo pip install mock
Install Pip3
sudo apt install libatlas-base-dev
pip install tensorflow
https://docs.aws.amazon.com/greengrass/latest/developerguide/ml-console.html#install-mxnet https://docs.aws.amazon.com/greengrass/latest/developerguide/ml-console.html#package-ml-model https://aws.amazon.com/blogs/iot/how-to-install-a-face-recognition-model-at-the-edge-device-with-aws-iot-greengrass/ https://docs.aws.amazon.com/greengrass/latest/developerguide/what-is-gg.html#ml-runtimes-libs https://docs.aws.amazon.com/greengrass/latest/developerguide/ml-inference.html#w7aac22c15c17 https://docs.aws.amazon.com/greengrass/latest/developerguide/lambda-group-config.html
https://www.amazon.com/Cylewet-Encoder-15%C3%9716-5-Arduino-CYT1062/dp/B06XQTHDRR https://new.siemens.com/global/en/company/stories/industry/lifesaving-drones-designed-with-cad-software.html
https://www.digitalistmag.com/iot/2018/04/23/digital-twin-technology-transforming-mill-industry-06090500/ I'm an Application Engineer here at Kepware. I'll try to help out with your query. Please follow this guide in setting OPC UA comms with Kepserver: https://www.kepware.com/getattachment/ccefc1a5-9b13-41e6-99d9-2b00cc85373e/opc-ua-client-server-easy-guide.pdf Regarding the Demo Timer, simply closing and then reopening the Kepserver will not be sufficient to overcome this. You would have to reset the demo timer by going to the following steps: Locate the green EX icon in the system tray (near windows clock) Right Click on the green EX icon and select Stop Runtime Process After runtime is stopped it will restart again automatically and in Kepserver you will received the following message: "Runtime connection lost, reconnect?" -> Select Yes You will received another message: "Update the runtime with the loaded project following connect?" - > Select Yes An additional window will appear asking for project saving. Select Yes or No depending on your needs.