Applied Machine Learning for IIoT and Smart Production—Methods to Improve Production Quality, Safety and Sustainability
Abstract
:1. Introduction
2. Safety and Security
- Security
- —Security ensures that the system is protected from unintended or unauthorized access, change, or destruction.
- Privacy
- —Privacy provides organizations control over the collection, processing, and storage of their information, by deciding how this information can be shared both within their own organization and with others.
- Reliability
- —Reliability guarantees that the system’s operation is uninterrupted and error-free for the specified time. Availability is related to reliability, but also takes into account planned operation stops.
- Safety
- – System Safety ensures that the people, property and environment are not at any unacceptable risk during the system’s operation.
- Resilience
- —System resilience provides a way to dynamically avoid, absorb and rapidly recover from changing adverse conditions. Resilience includes the ability to withstand and recover from deliberate attacks, accidents, or naturally occurring threats or incidents.
2.1. Datasets
2.2. Critics of Machine Learning Based Security
2.3. Summary of Security and Safety
3. Asset Localization
3.1. UWB
3.2. 5G
3.3. WiFi and Bluetooth Low Energy
3.4. Other
3.5. Summary of Asset Localization
- To learn the mapping between measurements and location
- To improve the accuracy of the location deduced by closed-form, geometrical problems
4. Quality Control
4.1. Visual Quality Inspection
4.2. Anomaly Detection
4.3. Datasets for Anomaly Detection
4.4. Summary of Machine Learning Based Quality Control
5. Maintenance
5.1. Tasks of Proactive Maintenance
- Fault detection
- — Detecting malfunctions is a complex task which involves several data sources such as equipment monitoring sensors, environment monitoring sensors, telemetry data, etc., in order to be able to recognize failures. The most common data that are gathered by sensors are: vibration monitoring, sound or acoustic monitoring and oil-analysis or lubricant monitoring [137,138].
- Diagnostics
- — Diagnostic processes are at the core of prognostics and strategy planning as they provide an analysis of failures and hazards, thus enabling the creation of models. One of the main task of diagnostics is Root cause analysis, which is a framework for investigating hazards and systematically discovering the possible root causes [139,140,141].
- Prognostics
- — The aim of prognostics is to estimate the future condition of equipment by modelling it based on the results of diagnostics. In most cases, the final goal of prognostics is to calculate the Remaining Useful Life (RUL) and Mean Time to Failure (MTTF). These factors play a key role in predicting and preventing possible future malfunctions and failures and help to schedule required maintenance tasks in time [142].
5.2. Fault Detection
5.3. Diagnostics
5.4. Prognostics
5.5. Manufacturing Optimization
5.6. Datasets for Smart Maintenance
5.7. The MANTIS Proactive Maintenance Platform
5.8. Summary of Machine Learning Based Maintenance and Manufacturing Optimization
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Application | Typical Machine Learning Techniques | References |
---|---|---|
Intrusion detection | Classification on network data (SVM, Bayes networks, decision tree, Random forest, neural network) | [25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42] |
Authentication | Classification on network data, Clustering | [25,46,47,48,49,50,51] |
Privacy leaking | Differential privacy and federated learning | [52,53,54] |
Data integrity | Latent space methods (Boltzmann-machine, DBN), Classification (Random Forest, SVM) | [55,56,57,58] |
Availability | Reinforcement learning and Neural networks (DBN, autoencoders) | [59,60,61] |
Offload security | Reinforcement learning | [62,63,64] |
Application | Typical Machine Learning Techniques | References |
---|---|---|
Learning mapping between measurements and location | kNN, SVM, Random Forest, XGBoost, Regression tree, neural networks, etc. | [79,82,83,86,87,88,89,90,91,92,93,94,95,96] |
Predicting non-LOS propagation | Neural network (CNN, TCN, etc.), SVM, Random Forests on channel impulse response | [72,73,74,75,76] |
Predicting location error | Neural network on channel impulse | [77,81,98] |
Application | Typical Machine Learning Techniques | References |
---|---|---|
Visual quality inspection | CNN (Yolo, VGG, ResNet, DenseNet), Autoencoders | [104,106,107,110,111,112,114] |
Anomaly detection | LSTM and PSO, kNN, SVM, PCA, XGBoost, Regressions, etc. | [117,122,125,126,127,129,129] |
Application | Typical Machine Learning Techniques | References |
---|---|---|
Fault Detection | KNN, SVM, Decision Tree, CNN | [143,144,145,146,147,148,149,150,151] |
Diagnostics | Decision Tree, Random Forest, KNN, SVM, CNN, RNN | [152,153,154,155,156,157,158,159,157] |
Prognostics | SVM, Bayesian Networks, RNN, CNN, Auto-Encoder, LSTM, Gated Recurrent Unit (GRM) | [160,161,162,163,164,165,166,167,168,169] |
Manufacturing optimization | Unsupervised learning (Regressions, SVM, GAN), Reinforcement learning (Q-learning, LSTM) | [170,171,173,174,175,177,178,181] |
Topic | Name of Dataset | Description |
---|---|---|
Smart maintenance | MetroPT [186] | Consists of samples of analog sensor signals (pressure, temperature, current consumption), digital signals (control signals, discrete signals), and GPS information (latitude, longitude, and speed). |
Alarm Logs in Packaging Industry (ALPI) [187] | Contains a sequence of alarms logged by packaging equipment in an industrial environment. The collection includes data logged by 20 machines, deployed in different plants around the world, from 21 February 2019 to 17 June 2020. | |
Quality inspection | UCI Machine Learning Repository [119] | A UCI collection of databases, domain theories, and data generators. There are several datasets from the manufacturing domain that are used for algorithm validation, including the semi-conductor domain. |
Outlier Detection DataSets [130] | ODDS provide access to a large collection of outlier detection datasets with ground truth (if available). The focus of the repository is to provide datasets from different domains including several manufacturing domains (wafer map). | |
Safety and security | KDD-99 dataset [65] | The dataset used for The Third International Knowledge Discovery and Data Mining Tools Competition, the competition task was to build a network intrusion detector algorithm. |
CSE-CIC-IDS2018 dataset [66] | The dataset includes seven different attack scenarios, namely Brute-force, Heartbleed, Botnet, DoS, DDoS, Web attacks, and infiltration of the network from inside. The attacking infrastructure includes 50 machines and the victim organization has 5 departments including 420 PCs and 30 servers. | |
CIC DDoS attack dataset [67] | The dataset contains different modern reflective DDoS attacks such as PortMap, NetBIOS, LDAP, MSSQL, UDP, UDP-Lag, SYN, NTP, DNS and SNMP. | |
Intrusion detection and privacy attack dataset [68,69] | Dataset for developing and evaluating different IEEE 802.11 Wi-Fi algorithms. | |
The University of Arizona datasets [70] | Different malware and network traffic datasets for developing and evaluating network security algorithms. | |
Localization | UTIL: An Ultra-wideband Time-difference-of-arrival Indoor Localization Dataset [194] | An Ultra-wideband Time-difference-of-arrival Indoor Localization Dataset. Raw sensor data including UWB TDOA, inertial measurement unit (IMU), optical flow, time-of-flight (ToF) laser, and millimeter-accurate ground truth data were collected during the flights of drones. |
CSI Dataset towards 5G NR High-Precision Positioning [195] | This dataset can be used for indoor positioning, indoor-outdoor-integrated positioning, NLoS, 5G channel estimation and other types of research, providing researchers with CSI-level position-related feature data. |
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Frankó, A.; Hollósi, G.; Ficzere, D.; Varga, P. Applied Machine Learning for IIoT and Smart Production—Methods to Improve Production Quality, Safety and Sustainability. Sensors 2022, 22, 9148. https://doi.org/10.3390/s22239148
Frankó A, Hollósi G, Ficzere D, Varga P. Applied Machine Learning for IIoT and Smart Production—Methods to Improve Production Quality, Safety and Sustainability. Sensors. 2022; 22(23):9148. https://doi.org/10.3390/s22239148
Chicago/Turabian StyleFrankó, Attila, Gergely Hollósi, Dániel Ficzere, and Pal Varga. 2022. "Applied Machine Learning for IIoT and Smart Production—Methods to Improve Production Quality, Safety and Sustainability" Sensors 22, no. 23: 9148. https://doi.org/10.3390/s22239148