===================== PdMLabs documentation ===================== **PdMLabs is a free and open-source Python library** created to help data scientists identify **early signs before events** of interest and model **time-to-event prediction**. .. grid:: 1 2 2 2 :gutter: 4 :padding: 2 2 0 0 :class-container: sd-text-center .. grid-item-card:: 📜 Our Manifesto :link: introduction :link-type: doc :class-header: bg-light Understand what PdMLabs is all about .. grid-item-card:: 🚀 Quick Start :link: getting-started/quickstart :link-type: doc :class-header: bg-light Get up and running in less than 5 minutes .. grid-item-card:: 💡 Examples :link: examples/index :link-type: doc :class-header: bg-light Explore real-world examples and use cases .. grid-item-card:: 📚 Concepts :link: concepts/index :link-type: doc :class-header: bg-light Learn about Experiments, Methodologies, and more .. grid-item-card:: 📖 API Reference :link: api-reference :link-type: doc :class-header: bg-light Complete API documentation Installation ------------ .. code-block:: bash pip install pdmlabs **Requirements**: Python >= 3.11 TL;DR ----- Load your data: .. code-block:: python from pdmlabs.utils.dataset import Dataset df=pd.read_csv("data/ims.csv") dataset_handler = Dataset(df,datetime_column="timestamp",train_sources=0.6,val_sources=0.2,test_sources=0.2) Train_Val_data, Train_Test_data = dataset_handler.get_unsupervised_dataset() # for early signs detection using anomaly detection # Depending on the goal of your analysis, you can also get the data for different modeling approaches: # Train_Val_data, _ = dataset_handler.get_rul_dataset() # for early signs detection using semi-supervised anomaly detection # Train_Val_data, _ = dataset_handler.get_rul_dataset() # for early signs detection using classification # Train_Val_data, _ = dataset_handler.get_rul_dataset() # for time-to-event prediction using regression # Train_Val_data, _ = dataset_handler.get_unsupervised_dataset() # for time-to-event prediction using Survival Analysis Perform your experiment (For guidance on how to choose the right experiment for your case, check the :doc:`concepts/index`): .. code-block:: python from pdmlabs.experiment.batch.auto_profile_semi_supervised_experiment import AutoProfileSemiSupervisedPdMExperiment from pdmlabs.RunExperiment import run_experiment # available Anomaly detection experiments: AutoProfileSemiSupervisedPdMExperiment,IncrementalSemiSupervisedPdMExperiment,UnsupervisedPdMExperiment,SemiSupervisedPdMExperiment # Classification experiments: SupervisedPdMExperiment # Time-to-event regression experiments: SupervisedRULPdMExperiment # Time-to-event with Survival Analysis experiments: Supervised_SA_PdMExperiment experiments = [AutoProfileSemiSupervisedPdMExperiment] experiment_names = ['My first experiment'] fit_size=1000 # initial data to fit, this is specific to AutoProfileSemiSupervisedPdMExperiment from pdmlabs.method.isolation_forest import IsolationForest from pdmlabs.method.lof_semi import LocalOutlierFactor methods = [IsolationForest,LocalOutlierFactor] param_space_dict_per_method = [{'n_estimators': [200,100],'max_samples': [200,100],'random_state': [42], 'max_features': [0.8,0.5],'bootstrap': [True,False]}, {'n_neighbors':[2,3,5,10,20]}] method_names = ["IF","LOF"] run_experiment(Train_Val_data, methods, param_space_dict_per_method, method_names, experiments, experiment_names, mlflow_port=5000, MAX_RUNS=4, MAX_JOBS=1, INITIAL_RANDOM=1, optimization_param="AD1_AUC", debug=True, maximize=maximize,profile_size=profile_for_test) .. code-block:: text Best score: 0.5741854636591478: 100%|██████████| 3/3 [00:03<00:00, 1.11s/it] My first experiment IF {'best_params': {'init_profile_size': 1000, 'method_bootstrap': True, 'method_max_features': 0.8, 'method_max_samples': 200, 'method_n_estimators': 100, 'method_random_state': 42, 'profile_size': 2}, 'best_objective': 0.5741854636591478} Best score: 0.6392039: 100%|██████████| 3/3 [00:03<00:00, 1.11s/it] My first experiment LOF {'best_params': {'n_neighbors': 8}, 'best_objective': 0.6392039} Process finished with exit code 0 Key Features ------------- .. grid:: 1 1 2 3 :gutter: 4 :padding: 2 2 0 0 .. grid-item-card:: ⚡ :class-header: bg-primary text-white Get instant alerts when your data quality drops below your defined thresholds .. grid-item-card:: Flexible :class-header: bg-primary text-white Choose from Classification, multiple Anomaly Detection flavors, Regression, and Survival Analysis modeling for finding early signs before events of interest or predicting time to event. .. grid-item-card:: Automated :class-header: bg-primary text-white Experiments leverage Bayesian Optimization to find the best model and hyperparameters for your data .. grid-item-card:: Extensible :class-header: bg-primary text-white Add custom models by implementing a simple interface. .. grid-item-card:: 📊 Rich Output :class-header: bg-primary text-white Keep track of experiments and check results using Mlflow dashboard. .. admonition:: Perfect for :class: tip - **Predictive Maintenance** monitoring sensor data for early signs of equipment failure - **Data Scientists** building robust models for time-to-event prediction - **Analytics Teams** seeking automated generation of insights from experimental analysis results The framework is particularly useful when you need to: - **Identify the most suitable methodology for a specific PdM case**, especially when the appropriate modeling approach or technique flavor is not obvious. - **Discover algorithm configurations that meet user-defined performance thresholds**, enabling the use of interpretable methods without sacrificing predictive capability. - **Analyze trade-offs between predictive performance and computational efficiency**, which is crucial when monitoring large fleets of assets or operating under resource or environmental constraints. - **Perform comparative evaluations of alternative solutions**, helping determine whether investing in a new method provides meaningful improvements over existing approaches. Next Steps ----------- .. toctree:: :hidden: :maxdepth: 1 🏠 Home .. toctree:: :hidden: :maxdepth: 2 :caption: Contents introduction concepts/index getting-started/index examples/index user-guide/index api-reference Ready to dive in? Here are some suggested paths: **New to PdMLabs?** → Start with :doc:`introduction` **Ready for action?** → Jump straight to the :doc:`getting-started/quickstart` **Eager to deepen understanding?** → Read :doc:`concepts/index` **Looking for examples?** → Check out :doc:`examples/index` **Need detailed configuration?** → Browse :doc:`user-guide/index` Publications --------------- Please cite the following papers when using PdMLabs for your research: - Anastasios Papadopoulos, Apostolos Giannoulidis, Anastasios Gounaris, and John Paparrizos. 2026. The Power of Anomaly Detection in Predictive Maintenance: [Experiments & Analysis]. Proc. ACM Manag. Data 4, 3, Article 242 (June 2026), 33 pages. https://doi.org/10.1145/3802119 .. code-block:: python @article{10.1145/3802119, author = {Papadopoulos, Anastasios and Giannoulidis, Apostolos and Gounaris, Anastasios and Paparrizos, John}, title = {The Power of Anomaly Detection in Predictive Maintenance: [Experiments \& Analysis]}, year = {2026}, issue_date = {June 2026}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {4}, number = {3}, url = {https://doi.org/10.1145/3802119}, doi = {10.1145/3802119}, journal = {Proc. ACM Manag. Data}, month = may, articleno = {242}, numpages = {33}, keywords = {predictive maintenance, time-series} } - Anastasios Papadopoulos, Apostolos Giannoulidis, Anastasios Gounaris, Towards AutoML Solutions for Predictive Maintenance, Procedia Computer Science, Volume 280, 2026, Pages 791-798, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2026.04.099. .. code-block:: python @article{PAPADOPOULOS2026791, title = {Towards AutoML Solutions for Predictive Maintenance}, journal = {Procedia Computer Science}, volume = {280}, pages = {791-798}, year = {2026}, note = {The 17th International Conference on Ambient Systems, Networks and Technologies Networks (ANT)/ the 9th International Conference on Emerging Data and Industry 4.0 (EDI40)}, issn = {1877-0509}, doi = {https://doi.org/10.1016/j.procs.2026.04.099}, url = {https://www.sciencedirect.com/science/article/pii/S1877050926011129}, author = {Anastasios Papadopoulos and Apostolos Giannoulidis and Anastasios Gounaris}, keywords = {Predictive Maintenance, Time-Series, AutoML}, } Acknowledgments --------------- PdMLabs is developed by Anastasios Papadopoulos and Apostolos Giannoulidis and supported by the Data Engineering (DELAB) Team of `Datalab AUTh `_, under the supervision of `Prof. Anastasios Gounaris `_. PdMLabs incorporates RUL and Survival Analysis evaluation components from the `TITEUF project `_. Licensed under the Apache License, Version 2.0.