PyITS

Welcome to PyITS

A Unified Python Toolkit for Industrial Time-Series Analytics

⦿ Motivation: Industrial Time-Series (ITS) analytics is essential across diverse sectors, including manufacturing, energy, transportation, and healthcare. While general time-series models have made significant advancements, ITS encompasses specialized tasks such as fault diagnosis, process monitoring, soft sensing, RUC prediction, and predictive maintenance. These tasks require unique data processing, training, and evaluation protocols, making existing toolkits unsuitable for ITS applications. Furthermore, implementation details for these protocols are often not disclosed in current literature. Therefore, the area of ITS analytics needs a dedicated toolkit. PyITS is created to fill in this blank.

⦿ Mission: PyITS is designed to be a versatile toolbox that streamlines the application of modern machine learning techniques for Industrial Time-Series (ITS) analytics. Our mission is twofold: (1) Enable engineers to quickly implement and compare existing algorithms, facilitating the efficient completion of ITS projects. (2) Provide researchers with a robust collection of updated baselines and standardized experimental protocols, allowing them to focus on algorithmic innovation. PyITS continuously integrates both classical and state-of-the-art machine learning algorithms, offering unified APIs across various models and tasks. Additionally, we provide comprehensive documentation and interactive tutorials to guide beginners through different applications.

🤗 Please star this repo to help others notice PyITS if you think it is a useful toolkit. This really means a lot to our open-source research. Thank you!

❖ Available Algorithms

PyITS supports fault diagnosis, process monitoring, soft sensing, RUC prediction, and predictive maintenance tasks on ITS datasets. Suppose T is the historical length, at the time-step $t$, the supported tasks are discribed as follows:

• SS: Soft sensor, which aims to estimate the value of the quality variable ($y_t$). Available input data include the historical values of process variables $x_{t-\mathrm{T}+1:t}$ and quality variable $y_{t-\mathrm{T}+1:t-1}$.
• PM: Process monitoring, which aims to predicts the next-step quality variable ($y_{t+1}$). Available input data include the historical values of process variables $x_{t-\mathrm{T}+1:t}$ and quality variable $y_{t-\mathrm{T}+1:t}$.
• FD: Fault diagnosis, which aims to determines the correct status of a given patch, including normal and various fault types. Available input data include the historical values of all variables $z_{t-\mathrm{T}+1:t}$.
• RUL: RUL prediction, which aims to forecasts the remaining time before a fault occurs. Available input data include the historical values of all variables $z_{t-\mathrm{T}+1:t}$.
• PdM: Predictive maintainance, which aims to estimates whether a fault occurrs within a specified future window. Available input data include the historical values of all variables $z_{t-\mathrm{T}+1:t}$.

The table below shows the availability of each algorithm for different tasks. The symbol ✅ indicates the algorithm is available for the corresponding task (note that some models are specifically designed for typical tasks, and PyITS modifies the data processing and output protocol to extend the spectrum of supported tasks).

Type	Model	Paper	SS	PM	FD	RUL	PdM	Remarks
Foundation model	Autoformer	NIPS2021	✅	✅	✅	✅	✅	Transformer-based time-series foundational model
Foundation model	Crossformer	ICLR2023	✅	✅	✅	✅	✅	Transformer-based time-series foundational model
Foundation model	ETSformer	arxiv	✅	✅	✅	✅	✅	Transformer-based time-series foundational model
Foundation model	FEDformer	ICML2022	✅	✅	✅	✅	✅	Transformer-based time-series foundational model
Foundation model	Informer	AAAI2021	✅	✅	✅	✅	✅	Transformer-based time-series foundational model
Foundation model	Nonstationary Transformer	NIPS2022	✅	✅	✅	✅	✅	Transformer-based time-series foundational model
Foundation model	PatchTST	ICLR2023	✅	✅	✅	✅	✅	Transformer-based time-series foundational model
Foundation model	Pathformer	ICLR2024	✅	✅	✅	✅	✅	Transformer-based time-series foundational model
Foundation model	PAttn	NIPS2024	✅	✅	✅	✅	✅	Transformer-based time-series foundational model
Foundation model	Pyraformer	ICLR2022	✅	✅	✅	✅	✅	Transformer-based time-series foundational model
Foundation model	Reformer	ICLR2020	✅	✅	✅	✅	✅	Transformer-based time-series foundational model
Foundation model	TimeXer	NIPS2024	✅	✅	✅	✅	✅	Transformer-based time-series foundational model
Foundation model	Transformer	NIPS2017	✅	✅	✅	✅	✅	Transformer-based time-series foundational model
Foundation model	iTransformer	ICLR2024	✅	✅	✅	✅	✅	Transformer-based time-series foundational model
Foundation model	FITS	arxiv	✅	✅	✅	✅	✅	MLP-based time-series foundational model
Foundation model	DLinear	AAAI2023	✅	✅	✅	✅	✅	MLP-based time-series foundational model
Foundation model	LightTS	PACM2023	✅	✅	✅	✅	✅	MLP-based time-series foundational model
Foundation model	FreTS	NIPS2023	✅	✅	✅	✅	✅	MLP-based time-series foundational model
Foundation model	TiDE	arxiv	✅	✅	✅	✅	✅	MLP-based time-series foundational model
Foundation model	TimeMixer	ICLR2024	✅	✅	✅	✅	✅	MLP-based time-series foundational model
Foundation model	Triformer	IJCAI2022	✅	✅	✅	✅	✅	MLP-based time-series foundational model
Foundation model	TSMixer	TMLR2023	✅	✅	✅	✅	✅	MLP-based time-series foundational model
Foundation model	Mamba	arxiv	✅	✅	✅	✅	✅	RNN-based time-series foundational model
Foundation model	LSTM		✅	✅	✅	✅	✅	RNN-based time-series foundational model
Foundation model	SegRNN	arxiv	✅	✅	✅	✅	✅	RNN-based time-series foundational model
Foundation model	MICN	ICLR2023	✅	✅	✅	✅	✅	CNN-based time-series foundational model
Foundation model	SCINet	NIPS2022	✅	✅	✅	✅	✅	CNN-based time-series foundational model
Foundation model	TCN	arxiv	✅	✅	✅	✅	✅	CNN-based time-series foundational model
Foundation model	TimesNet	ICLR2023	✅	✅	✅	✅	✅	CNN-based time-series foundational model
Foundation model	Koopa	NIPS2023	✅	✅	✅	✅	✅	Other time-series foundational model
Foundation model	FiLM	NIPS2022	✅	✅	✅	✅	✅	Other time-series foundational model
Task-specific model	DLSTM	TII2022	✅	✅	✅	✅	✅	Originally developed for SS task
Task-specific model	DTGRU	TII2023	✅	✅	✅	✅	✅	Originally developed for RUL task
Task-specific model	AdaNet	TII2024	✅	✅	✅	✅	✅	Originally developed for RUL task
Task-specific model	DeepPLS	TNNLS2023	✅	✅		✅		Originally developed for PdM task
Task-specific model	RSN	TII2023	✅	✅	✅	✅	✅	Originally developed for FD task
Task-specific model	MCN	TSMC2024	✅	✅	✅	✅	✅	Originally developed for FD task
Task-specific model	MCTAN	TNNLS2023	✅	✅	✅	✅	✅	Originally developed for SS task
Task-specific model	DLformer	TNNLS2024	✅	✅	✅	✅	✅	Originally developed for RUL task
Task-specific model	TR-LT	TII2022	✅	✅	✅	✅	✅	Originally developed for RUL task

✨ Contribute your model right now to enhance your research impact! Your work will be widely used and cited by the community. Refer to the models/Transformer.py to see how to devise your model with PyITS.

❖ PyITS Composition

The PyITS framework consists of three main components: data_provider, model, and estimator. This modular architecture ensures flexibility, scalability, and ease of use across diverse ITS applications.

• Data provider: it aims to process the dataset with diverse features and protocols to provide appropriate inputs for different ITS tasks. It inherits from the base class data_provider/Base_Dataset, which standardizes data handling procedures across the framework. Each dataset has a unique data_provider class, while each ITS task to execute corresponds to a specific in-class method within the data_provider class.
• Model: it serves as the encoder to generate representations from the input data provided by Data provider. Leveraging advanced machine learning algorithms, the model captures intricate dependencies inherent in ITS data. Notably, the models are generally designed to be task-agnostic, allowing it to be used across different tasks.
• Estimator: it wraps the data_provider, model, and decoder for a given task, defining the training and evaluation protocols. The decoder, which is cascaded from the model, transforms the encoded representations into task-specific outputs. For example, in the SS task, the decoder is an affine layer that transforms the representation $z$ into a real-valued estimate of the quality variable $y$; in the PdM task, the decoder is an affine layer followed by a softmax layer that transforms $z$ into a probability vector, indicating the likelihood of anomalies occurring at corresponding time stamps.

❖ Installation

We will release a package on PyPI and anaconda very soon. Before that, you can install PyITS by cloning it.

git clone https://github.com/Master-PLC/PyITS.git

❖ Contribution

We warmly invite you to contribute to PyITS. By committing your code:

1. You can make your works readily available for PyITS users to run, which helps your work gain more recognition and credits.
2. You will be listed as a PyITS contributor and a volunteer developer.
3. Your contributions will be highlighted in PyITS release notes.

Refer to the data_provider/data_generator.py, models/Transformer.py and estimator/foundation/soft_sensor_estimator.py to see how to include your dataset, model and estimator in PyITS.

❖ Available Datasets

Dataset	Abbreviation	SS	PM	FD	RUL	PdM
Sulfur Recovery Unit	SRU	✅	✅
Debutanizer Column	DC	✅	✅

❖ Acknowledgement

• We sincerely thank the authors of the book Soft Sensors for Monitoring and Control of Industrial Processes for generously providing the Debutanizer Column and Sulfur Recovery Unit datasets.