HiMAP: Hidden Markov models for Advanced Prognostics

HiMAP is a Python package for implementing hidden Markov Models (HMMs) and hidden semi-Markov Models (HSMMs) tailored for prognostic applications. It provides a probabilistic framework for predicting Remaining Useful Life (RUL) and modeling complex degradation processes without requiring labeled datasets.

Key Features:

  • HMM and HSMM implementations: Unsupervised stochastic models for system degradation.

  • Core Methods: Includes methods for model training, state inference, and data generation through Monte Carlo sampling.

  • Probabilistic RUL prediction: computes RUL as a probability density function (pdf) using Viterbi-decoded state sequences.

Installation:

Option 1: Install via pip

The easiest way to install HiMAP is through pip. Note that to install the package you need Python 3.9+. Simply run the following command:

pip install himap

Option 2: Install from source

If you prefer to install HiMAP directly from the source, follow these steps: 1. Create a virtual environment and activate it. (This example will be demonstrated with Anaconda, but it is not required.)

  • Step 1a:

conda create -n himap_env python=3.9 -y

  • Step 1b:

conda activate himap_env

  1. This repository can be directly pulled through GitHub by the following commands:

  • Step 2a:

conda install git

  • Step 2b:

git clone https://github.com/GroupiSP/himap.git

  • Step 2c:

cd himap

  1. The dependencies can be installed using the requirements.txt file

pip install -r requirements.txt

  1. To compile the Cython code, run the following command

python setup_cython.py build_ext --inplace

Documentation overview:

Citing this repository:

If you use HiMAP in your research, please use the following citation:

@software{kontogiannis_2026_18418216,
   author = {Kontogiannis, Thanos and Salinas-Camus, Mariana and Eleftheroglou, Nick},
   title  = {HiMAP: Hidden Markov models for Advanced Prognostics},
   month  = jan,
   year   = 2026,
   publisher = {Zenodo},
   version = {v1.3.0},
   doi = {10.5281/zenodo.18418216},
   url = {https://doi.org/10.5281/zenodo.18418216},
   swhid = {swh:1:dir:447c3c9e6743e4b56015f2107dbcd6d0eebe1bda
                ;origin=https://doi.org/10.5281/zenodo.18418215;vi
                sit=swh:1:snp:66b6fd9f335c7a71c1d805bc6cf19261589a
                0770;anchor=swh:1:rel:3c6b8da54a15799b589065dbedc6
                5540af805806;path=GroupiSP-himap-103efdc
               },
}

Authors

HiMAP was developed by the Intelligent System Prognostics (ISP) group at TU Delft, Aerospace Engineering Faculty.

ISP TU