AI w przemyśle

Industrial AI · id industrialai · aliases: Industrial AI, AI in manufacturing, AI w produkcji, Industry 4.0 AI, smart manufacturing, przemysłowa sztuczna inteligencja, Przemysł 4.0

Zastosowania uczenia maszynowego w produkcji i przemyśle procesowym: utrzymanie predykcyjne, kontrola jakości, cyfrowe bliźniaki, sterowanie procesem i planowanie produkcji.

Machine learning applied to manufacturing and process industries: predictive maintenance, quality inspection, digital twins, process control and production scheduling.

In one sentence

Programy, które pilnują maszyn w fabryce, oglądają wyroby na taśmie, podpowiadają ustawienia procesu i układają plan produkcji.

Software that watches over factory machines, inspects products on the line, suggests process settings and plans production.

Problem it solves

Awaria maszyny zatrzymuje całą linię, wadliwe wyroby trafiają do klientów, a doświadczeni technicy odchodzą na emeryturę razem ze swoją wiedzą.

One broken machine stops the whole line, faulty products reach customers, and experienced technicians retire taking their know-how with them.

What it does

Jak brygadzista z tysiącem oczu i uszu: słyszy, że łożysko zaczyna szumieć, zanim pęknie, i widzi rysę, której człowiek by nie zauważył.

Like a foreman with a thousand eyes and ears: it hears a bearing start humming before it breaks and spots scratches people miss.

Everyday example

W piekarni przemysłowej kamera nad taśmą odrzuca przypalone bułki, a czujnik na silniku pieca ostrzega, że wkrótce trzeba wymienić łożysko.

In an industrial bakery a camera over the belt rejects burnt rolls, while a sensor on the oven motor warns that a bearing will soon need replacing.

This concerns you if you…

pracujesz w fabryce, zakładzie lub utrzymaniu ruchu
odpowiadasz za jakość wyrobów albo przestoje maszyn
słyszysz o inteligentnych fabrykach i cyfrowych bliźniakach
work in a factory, plant or maintenance team
are responsible for product quality or machine downtime
hear about smart factories and digital twins

Subtopics

Asystenci językowi dla przemysłu (LLM assistants for manufacturing and maintenance): Language models in manufacturing: search over documentation and work orders, controller code generation, and support for design and quality control.
- Analiza notatek serwisowych i zleceń napraw (technical language processing) (Technical language processing of maintenance work orders): A domain-driven approach to NLP for engineering text, especially maintenance work orders, built on vocabularies, entity types and human-in-the-loop annotation.
- Asystent awarii oparty na instrukcjach serwisowych (RAG) (Retrieval-augmented troubleshooting over technical manuals): Applying RAG to fault diagnosis: the language model's answer is grounded in technical-manual passages or fault-code tables.
- Generowanie kodu sterowników PLC z modelami językowymi (LLM-based PLC code generation (IEC 61131-3)): Generating controller programs (e.g. IEC 61131-3 Structured Text) with an LLM in a loop with a compiler and formal verification.
Cyfrowe bliźniaki (Digital twins): A virtual representation of a physical asset synchronised with its data in both directions, used for monitoring, scenario simulation and optimisation.
- Modele hybrydowe: fizyka plus dane (grey-box) (Hybrid physics–data (grey-box) modelling): Models that couple mechanistic submodels (e.g. mass and energy balances) with nonparametric data-driven components, in serial or parallel arrangements.
- Modele zredukowane do bliźniaków w czasie rzeczywistym (Reduced-order models for real-time digital twins): Cutting the degrees of freedom of a high-fidelity model while keeping the required accuracy, to enable real-time simulation.
- Synchronizacja bliźniaka z maszyną (estymacja stanu) (Digital twin state estimation and data assimilation): Bayesian updating of a twin model's state and parameters from the stream of sensor observations of the physical asset.
Harmonogramowanie produkcji (Production scheduling with learning-based methods): Assigning and sequencing operations on machines (e.g. job shop) with learning, including dispatching policies trained with RL on a graph representation.
- Ewolucja reguł priorytetów produkcji (programowanie genetyczne) (Genetic programming hyper-heuristics for dispatching rules): Hyper-heuristics in which genetic programming automatically designs priority functions for dynamic production scheduling.
- Uczenie reguł kolejkowania zadań z RL i sieciami grafowymi (Deep reinforcement learning dispatching for job-shop scheduling): Learning a policy that picks the next operation in job-shop scheduling (JSSP) with deep RL over a disjunctive-graph representation.
Optymalizacja i sterowanie procesów (Process optimization and control with machine learning): Machine learning in process industries for modelling, optimisation and control: hybrid models with mathematical programming, and reinforcement learning.
- Czujniki wirtualne (soft sensors) (Data-driven soft sensors): Regression models estimating hard-to-measure process variables (quality, composition) in real time from readily available measurements.
- Optymalizacja bayesowska parametrów procesu i receptur (Bayesian optimization of process parameters and recipes): Sequential optimisation of an expensive objective (e.g. process yield) using a probabilistic surrogate model and an acquisition function.
- Sterowanie instalacjami z uczeniem ze wzmocnieniem (Reinforcement learning for industrial and building control): Reinforcement learning controlling real installations (cooling, HVAC, processes), with emphasis on learning from offline data, operational constraints and evaluation.
- Sterowanie predykcyjne z modelem uczonym na danych (MPC) (Learning-based model predictive control): Receding-horizon control in which the MPC prediction model, cost parameters or safety layer are learned from data.
Utrzymanie predykcyjne maszyn (Predictive maintenance and prognostics (PHM)): Monitoring equipment condition from sensor data to detect, diagnose and forecast faults and schedule maintenance before failure.
- Diagnostyka maszyn z drgań i dźwięku (Machine fault diagnosis from vibration and acoustic signals): Classifying fault type and location (bearings, gears, imbalance) from vibration, current and acoustic signals, with classical methods and deep learning.
  - Przenoszenie diagnostyki na inne maszyny i warunki pracy (Cross-domain transfer learning for machine fault diagnosis): Unsupervised domain adaptation of fault classifiers from labelled source data to unlabelled data from other conditions or machines.
- Szacowanie pozostałego czasu życia (RUL) (Remaining useful life (RUL) prediction): Regressing the cycles or operating time left before a failure threshold, from multivariate sensor time series.
  - Prognoza czasu do awarii z estymacją niepewności (Uncertainty-aware remaining useful life prediction): Forecasting a distribution, not just a value, of remaining useful life, e.g. with deep Gaussian processes, network ensembles or Monte Carlo dropout.
Wizyjna kontrola jakości (Automated visual inspection): Detecting, localising and classifying product defects in industrial camera images with computer vision, with or without defect labels.
- Generowanie syntetycznych obrazów wad (Few-shot synthetic defect image generation): Generating defect image–mask pairs from a few defect examples and many normal images, as augmentation for training detectors.
- Wykrywanie wad bez przykładów z modelem wizja–język (Zero- and few-shot anomaly detection with vision-language models): Anomaly classification and segmentation with no training data for the category, or a few normal images, by matching image features to text descriptions.
- Wykrywanie wad uczone tylko na dobrych sztukach (Unsupervised industrial anomaly detection): Detecting and localising image anomalies when training only on nominal samples, usually by comparing features with a model of normality.
- Wykrywanie wad w skanach 3D i chmurach punktów (3D and multimodal industrial anomaly detection): Unsupervised anomaly detection and localisation in point clouds or depth maps, often fused with RGB images.

Sources

Industrial AI: Applications with Sustainable Performance · Jay Lee
Machine learning for industrial sensing and control: A survey and practical perspective · Nathan P. Lawrence, Seshu Kumar Damarla, Jong Woo Kim, Aditya Tulsyan, Faraz Amjad, Kai Wang, Benoit Chachuat, Jong Min Lee, Biao Huang, R. Bhushan Gopaluni
Large Language Models for Manufacturing · Yiwei Li, Huaqin Zhao, Hanqi Jiang i in. (24 autorów, kier. Tianming Liu)