Time to failure (TTF) using Weibull distribution and recurrent neural networks in Keras.
I am using a dataset from Microsoft that is composed of 5 csv files (a dataset collected by Fidan Boylu Uz from Microsoft).
mkdir -p ml/data/datasetcd ml/data/dataset
wget https://azuremlsampleexperiments.blob.core.windows.net/datasets/PdM_telemetry.csv
wget https://azuremlsampleexperiments.blob.core.windows.net/datasets/PdM_errors.csv
wget https://azuremlsampleexperiments.blob.core.windows.net/datasets/PdM_maint.csv
wget https://azuremlsampleexperiments.blob.core.windows.net/datasets/PdM_failures.csv
wget https://azuremlsampleexperiments.blob.core.windows.net/datasets/PdM_machines.csvPlease download all 5 csv files into ml/data/dataset folder.
telemetry.csv The first data source is the telemetry time-series data which consists of voltage, rotation, pressure and vibration measurements collected from 100 machines in real time averaged over every hour collected.
errors.csv The errors logs are non-breaking errors thrown while the machine is still operational and do not constitute as failures. The error date and times are rounded to the closest hour since the telemetry data is collected at an hourly rate.
maint.csv This file contains the scheduled and unscheduled maintenance records which correspond to both regular inspection of components as well as failures. A record is generated if a component is replaced during the scheduled inspection or replaced due to a break down. The records that are created due to break downs will be called failures which is explained in the later sections.
machines.csv This data set includes some information about the machines which are model type and years in service.
failures.csv These are the records of component replacements due to failures. Each record has a date and time, machine ID and failed component type.
cd mlconda create --name predictive-maintenance python=3.6source activate predictive-maintenancepip install -r requirements.txt