PyPHM - Machinery data, made easy
Why build an open-source package for machinery data?
Condition MonitoringData Science
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Articles about machine learning, manufacturing, and data science
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14 articles about machine learning, manufacturing, and data science
Beautiful Plots: The Violin
Music to my ears... err... eyes? The violin plot is a worthy tool for any data visualization tool box. Let's build one, in Plotly, as we explore historic birth trends in the USA!
PlotlyViolin PlotData Visualization
Using Jupyter Notebooks on a High Performance Computer - Tutorial
Using Jupyter Notebooks in a high performance computing environment is easy! This tutorial will show you how.
HPCJupyter NotebookTutorial
Analyzing the Results - Advances in Condition Monitoring, Pt VII
We've trained the variational autoencoders, and in this post, we see how the models perform in anomaly detection. We check both the input and latent space anomaly detection effectiveness.
Precision Recall CurveROC CurveMachine Learning
Beautiful Plots: The Lollipop
The lollipop chart is great at visualizing differences in variables along a single axis. In this post, we create an elegant lollipop chart, in Matplotlib, to show the differences in model performance.
MatplotlibLollipop ChartData Visualization
Beautiful Plots: The Decision Boundary
How can we communicate complex concepts using data visualization tools? In this first post -- in a series titled 'Beautiful Plots' -- we build an elegant chart demonstrating the decision boundary from a KNN classifier.
MatplotlibKNNData Visualization
Building a Variational Autoencoder - Advances in Condition Monitoring, Pt VI
In this post, we'll explore the variational autoencoder (VAE) and see how we can build one for use on the UC Berkeley milling data set. A variational autoencoder is more expressive than a regular autoencoder, and this feature can be exploited for anomaly detection.
Machine LearningCondition MonitoringVariational Autoencoder
Data Exploration - Advances in Condition Monitoring, Pt V
Data exploration is an important first step in any new data science problem. In this post we introduce a metal machining data set that we'll use to test anomaly detection methods. We'll explore the data set, see how it is structured, and do some data visualization.
AIMachine LearningCondition Monitoring
Anomaly Detection - Advances in Condition Monitoring, Pt IV
Machines fail. Anomaly detection, using an autoencoder, can be used to identify failures that have never been seen before.
AIMachine LearningCondition Monitoring
The Case for Feature Engineering - Advances in Condition Monitoring, Pt III
Feature engineering can be powerful tool, but it doesn't have to be complicated. Often, the simple solution is best.
Machine LearningCondition MonitoringFeature Engineering
Data-Driven Methods - Advances in Condition Monitoring, Pt II
In part two, we give an overview of the two data-driven approaches in condition monitoring; that is, feature engineering and end-to-end deep learning. Which approach should you use? Well, it all depends...
ManufacturingMachine LearningCondition Monitoring
The Business Case - Advances in Condition Monitoring, Pt I
Why care about data-driven condition monitoring techniques? In this post, we articulate the business case for using these advanced methods in an industrial environment.
ManufacturingMachine LearningCondition Monitoring
