The library `phmd` is designed to facilitate access to datasets in the context of industrial prognosis and health management (PHM). The main goal is to streamline automated processing, enabling researchers and practitioners to obtain, manipulate, and analyze data relevant to their work in an easy maner.
In the realm of predictive maintenance and fault diagnosis, the ability to efficiently search and access relevant datasets is crucial for developing effective machine learning models. The phmd library provides a straightforward method to search through a comprehensive collection of datasets based on specific features or criteria. This functionality allows users to quickly locate datasets that match their needs, enhancing the efficiency of their research and development processes.
This library provides tools for data search, downloading, and loading without concerns about the locations or source formats of the datasets. In a nutshell, the library provides a unified interface for accessing an important number of datasets from diferent sources and heterogeneous format, thereby reducing significantly the time and effort required to prepare datasets for machine learning and other analytical tasks.
### Software functionalities
The main features of the developed software can be summarized as follows:
- **Listing and search**. The library allows users to search for datasets tailored to specific purposes. Each dataset is associated with a set of meta-attributes facilitating the selection of the most suitable dataset for a particular research objective.
- **Automatic download**. The download process is automatically triggered when a dataset is required to load only if it has not been previously downloaded.
- **Loading**. One of the most important features of this library is its dataset loading capability. The loading functionality abstracts away from source format in which the dataset is originally provided, making it agnostic to the user and allowing them to focus directly on data analysis after loading. The loading functionality accepts the dataset name and the task type as parameters. Based on the specified task, the target variable is automatically computed, and the relevant features are filtered accordingly.
### Installation
You can install `phmd` with pip:
```
pip install phmd
```
or clone this repository and install it in editable mode:
```
git clone https://github.com/dasolma/phmd
cd phmd
pip install -e .
```
### Examples of usage
#### Search datasets
The method *search* of the class *Dataset* allow list of the dataset avaiable or search for datasets that match any criteria.
For example searching for datasets that contain vibration data, users can identify various datasets across different domains and applications, such as mechanical or manufacturing contexts. The following example illustrates how to perform a dataset search using the phmd library, showcasing the relevant details, including dataset names, domains, application areas, task names, and the nature of the data and features provided.
```
>>> import phmd
>>> datasets.Dataset.search(features='vibra')
name domain nature app task name [target] data nature features
------- ------------- ----------- ------- ------------------ ------------ --------
CWRU Mechanical time-series Bearing Diagnosis [fault] time-series vibration
DFD15 Manufacturing time-series Drill Diagnosis [fault] time-series vibration
DFD15 Manufacturing time-series Drill Stage [stage] time-series vibration
...
...
...
UPM23 Mechanical time-series Bearing Diagnosis [fault] time-series vibration
XJTU-SY Mechanical time-series Bearing Prognosis [rul] time-series vibration
XJTU-SY Mechanical time-series Bearing Diagnosis [fault] time-series vibration
```
### Get information of a dataset
Understanding the specifics of a dataset is vital for effective analysis and model development. The phmd library simplifies this process by providing a straightforward way to retrieve detailed information of the dataset collection. By utilizing the Dataset class, users can obtain comprehensive descriptions, system information, features, tasks, resources, and references associated with a particular dataset.
In the following example, we demonstrate how to access detailed information for the well-known CWRU (Case Western Reserve University) dataset, which focuses on bearing fault diagnosis. The output includes essential details such as the dataset's description, the types of sensors used, the nature of the data, and storage requirements. This allows users to evaluate the dataset's suitability for their specific applications and research needs.
```
>>> from phmd import datasets
>>> ds = datasets.Dataset("CWRU")
>>> print(ds.describe())
Description
===========
In this renowned dataset, experiments were conducted utilizing
a 2 HP Reliance Electric motor, where acceleration data was
measured at locations both near to and remote from the motor...
Designation: Bearing Fault Diagnostic
Publisher: Case Western Reserve University
Domain: Mechanical component
Application: Bearing
System info
===========
1. type : Rotatory machine : bearing
2. sensors : Voltmeter, ammeter and thermocouple sensor suite
3. bearing : 6205-2RSL JEM SKF deep-groove ball bearing (and NTN equivalent)
Features
========
BA :
description : base accelerometer data
type : vibration
DE :
description : drive end accelerometer data
type : vibration
FE :
description : fan end accelerometer data
type : vibration
Tasks
=====
Diagnosis :
features : DE
identifier : unit
min_ts_len : 63788
nature : time-series
num_units : 161
target : fault
target_distribution : 0.24,0.51,0.23,0.021
target_labels : IR,OR,BA,NO
type : classification:multiclass
Resources
=========
1. storage :
a) zipped : 246MB
b) unzipped : 689MB
c) RAM :
Data set (full) : 5.2GB
2. load time (SSD disk) :
a) unzipped :
Data set (full) : 3s
a) zipped :
Data set (full) : 7s
References
==========
citation : K.A. Loparo, Bearings vibration data set.
The Case Western Reserve University Bearing
Data Center. https://engineering.case.edu
manual download : https://engineering.case.edu/bearingdatacenter
```
### Load a dataset
Loading a dataset is a crucial step in any data analysis or machine learning workflow. The phmd library streamlines this process, allowing users to effortlessly load datasets and access relevant tasks and features. In this example, we demonstrate how to load the CWRU dataset.
By initializing the Dataset class with the dataset name, users can retrieve specific tasks associated with the dataset. In this case, we access the 'fault' task for analysis. The subsequent command loads the first dataset subset associated with this task.
```
>>> ds = datasets.Dataset("CWRU")
>>> task = ds['fault']
>>> sets = task[0]
Dataset CWRU already downloaded and extracted
Remember to cite the original publisher dataset:
@misc{caseBearingData,
author = {},
title = {{B}earing {D}ata {C}enter | {C}ase {S}chool of {E}ngineering
{C}ase {W}estern {R}eserve {U}niversity --- engineering.case.edu},
howpublished = {\url{https://engineering.case.edu/bearingdatacenter}},
year = {},
note = {[Accessed 08-04-2024]},
}
You can download the dataset manually from: https://engineering.case.edu/bearingdatacenter
** If you find this tool useful, please cite our SoftwareX paper.
Reading data: 100%|██████████| 161/161 [00:03<00:00, 47.43it/s]
INFO:root:Read in 5.96511435508728 seconds
```
The output informs the user about the status of the dataset, including whether it has already been downloaded and extracted. It also provides a citation format for referencing the dataset in future work, as well as a manual download link. Additionally, the process logs detailed information about data reading and feature extraction, allowing users to monitor performance and resource usage during the loading process.
## ACKNOWLEDGMENT
This work has been supported by Grant PID2019-109152GBI00/AEI/10.13039/501100011033 (Agencia Estatal de Investigacion), Spain and by the Ministry of Science and Education of Spain through the national program "Ayudas para contratos para la formacion de investigadores en empresas (DIN2019)", of State Programme of Science Research and Innovations 2017-2020.
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"description": "\nThe library `phmd` is designed to facilitate access to datasets in the context of industrial prognosis and health management (PHM). The main goal is to streamline automated processing, enabling researchers and practitioners to obtain, manipulate, and analyze data relevant to their work in an easy maner. \n\nIn the realm of predictive maintenance and fault diagnosis, the ability to efficiently search and access relevant datasets is crucial for developing effective machine learning models. The phmd library provides a straightforward method to search through a comprehensive collection of datasets based on specific features or criteria. This functionality allows users to quickly locate datasets that match their needs, enhancing the efficiency of their research and development processes.\n\nThis library provides tools for data search, downloading, and loading without concerns about the locations or source formats of the datasets. In a nutshell, the library provides a unified interface for accessing an important number of datasets from diferent sources and heterogeneous format, thereby reducing significantly the time and effort required to prepare datasets for machine learning and other analytical tasks.\n\n### Software functionalities\n\nThe main features of the developed software can be summarized as follows:\n\n- **Listing and search**. The library allows users to search for datasets tailored to specific purposes. Each dataset is associated with a set of meta-attributes facilitating the selection of the most suitable dataset for a particular research objective.\n- **Automatic download**. The download process is automatically triggered when a dataset is required to load only if it has not been previously downloaded. \n- **Loading**. One of the most important features of this library is its dataset loading capability. The loading functionality abstracts away from source format in which the dataset is originally provided, making it agnostic to the user and allowing them to focus directly on data analysis after loading. The loading functionality accepts the dataset name and the task type as parameters. Based on the specified task, the target variable is automatically computed, and the relevant features are filtered accordingly.\n\n\n### Installation\n\nYou can install `phmd` with pip:\n\n```\npip install phmd\n```\n\nor clone this repository and install it in editable mode:\n\n```\ngit clone https://github.com/dasolma/phmd\ncd phmd\npip install -e .\n```\n\n\n### Examples of usage\n\n#### Search datasets\n\nThe method *search* of the class *Dataset* allow list of the dataset avaiable or search for datasets that match any criteria.\n\nFor example searching for datasets that contain vibration data, users can identify various datasets across different domains and applications, such as mechanical or manufacturing contexts. The following example illustrates how to perform a dataset search using the phmd library, showcasing the relevant details, including dataset names, domains, application areas, task names, and the nature of the data and features provided.\n\n```\n>>> import phmd\n>>> datasets.Dataset.search(features='vibra')\n\nname domain nature app task name [target] data nature features\n------- ------------- ----------- ------- ------------------ ------------ --------\nCWRU Mechanical time-series Bearing Diagnosis [fault] time-series vibration\nDFD15 Manufacturing time-series Drill Diagnosis [fault] time-series vibration\nDFD15 Manufacturing time-series Drill Stage [stage] time-series vibration\n...\n...\n...\nUPM23 Mechanical time-series Bearing Diagnosis [fault] time-series vibration\nXJTU-SY Mechanical time-series Bearing Prognosis [rul] time-series vibration\nXJTU-SY Mechanical time-series Bearing Diagnosis [fault] time-series vibration\n```\n\n### Get information of a dataset\n\nUnderstanding the specifics of a dataset is vital for effective analysis and model development. The phmd library simplifies this process by providing a straightforward way to retrieve detailed information of the dataset collection. By utilizing the Dataset class, users can obtain comprehensive descriptions, system information, features, tasks, resources, and references associated with a particular dataset.\n\nIn the following example, we demonstrate how to access detailed information for the well-known CWRU (Case Western Reserve University) dataset, which focuses on bearing fault diagnosis. The output includes essential details such as the dataset's description, the types of sensors used, the nature of the data, and storage requirements. This allows users to evaluate the dataset's suitability for their specific applications and research needs.\n\n```\n>>> from phmd import datasets\n>>> ds = datasets.Dataset(\"CWRU\")\n>>> print(ds.describe())\n\nDescription\n===========\nIn this renowned dataset, experiments were conducted utilizing \na 2 HP Reliance Electric motor, where acceleration data was \nmeasured at locations both near to and remote from the motor...\n\nDesignation: Bearing Fault Diagnostic\nPublisher: Case Western Reserve University\nDomain: Mechanical component\nApplication: Bearing\n\nSystem info\n===========\n1. type : Rotatory machine : bearing\n2. sensors : Voltmeter, ammeter and thermocouple sensor suite\n3. bearing : 6205-2RSL JEM SKF deep-groove ball bearing (and NTN equivalent)\n\nFeatures\n========\nBA :\n description : base accelerometer data\n type : vibration\nDE :\n description : drive end accelerometer data\n type : vibration\nFE :\n description : fan end accelerometer data\n type : vibration\n\nTasks\n=====\nDiagnosis :\n features : DE\n identifier : unit\n min_ts_len : 63788\n nature : time-series\n num_units : 161\n target : fault\n target_distribution : 0.24,0.51,0.23,0.021\n target_labels : IR,OR,BA,NO\n type : classification:multiclass\n\nResources\n=========\n1. storage :\n a) zipped : 246MB\n b) unzipped : 689MB\n c) RAM :\n Data set (full) : 5.2GB\n2. load time (SSD disk) :\n a) unzipped :\n Data set (full) : 3s\n a) zipped :\n Data set (full) : 7s\n\nReferences\n==========\ncitation : K.A. Loparo, Bearings vibration data set. \n The Case Western Reserve University Bearing \n Data Center. https://engineering.case.edu\nmanual download : https://engineering.case.edu/bearingdatacenter\n```\n\n### Load a dataset\n\nLoading a dataset is a crucial step in any data analysis or machine learning workflow. The phmd library streamlines this process, allowing users to effortlessly load datasets and access relevant tasks and features. In this example, we demonstrate how to load the CWRU dataset.\n\nBy initializing the Dataset class with the dataset name, users can retrieve specific tasks associated with the dataset. In this case, we access the 'fault' task for analysis. The subsequent command loads the first dataset subset associated with this task.\n\n```\n>>> ds = datasets.Dataset(\"CWRU\")\n>>> task = ds['fault']\n>>> sets = task[0]\n\nDataset CWRU already downloaded and extracted\nRemember to cite the original publisher dataset:\n\t@misc{caseBearingData,\n\t\tauthor = {},\n\t\ttitle = {{B}earing {D}ata {C}enter | {C}ase {S}chool of {E}ngineering \n\t\t {C}ase {W}estern {R}eserve {U}niversity --- engineering.case.edu},\n\t\thowpublished = {\\url{https://engineering.case.edu/bearingdatacenter}},\n\t\tyear = {},\n\t\tnote = {[Accessed 08-04-2024]},\n\t}\nYou can download the dataset manually from: https://engineering.case.edu/bearingdatacenter\n\n** If you find this tool useful, please cite our SoftwareX paper.\n\nReading data: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 161/161 [00:03<00:00, 47.43it/s]\nINFO:root:Read in 5.96511435508728 seconds\n```\n\nThe output informs the user about the status of the dataset, including whether it has already been downloaded and extracted. It also provides a citation format for referencing the dataset in future work, as well as a manual download link. Additionally, the process logs detailed information about data reading and feature extraction, allowing users to monitor performance and resource usage during the loading process.\n\n## ACKNOWLEDGMENT\n\nThis work has been supported by Grant PID2019-109152GBI00/AEI/10.13039/501100011033 (Agencia Estatal de Investigacion), Spain and by the Ministry of Science and Education of Spain through the national program \"Ayudas para contratos para la formacion de investigadores en empresas (DIN2019)\", of State Programme of Science Research and Innovations 2017-2020.\n",
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