Artificial Intelligence for Predictive Maintenance Applications: Key Components, Trustworthiness, and Future Trends
Abstract
:1. Introduction
Research Questions
- What are the main components of AI-based PdM systems?
- What are the state-of-the-art (SOTA) PdM methods? Regarding accuracy, cost-effectiveness, and scale, what are their advantages?
- What are the advantages of AI-based PdM techniques over traditional techniques regarding performance and cost-effectiveness?
- What are the challenges and limitations of AI-based PdM?
- How can AI-based PdM systems ensure high transparency and explanation?
- How can AI be integrated into existing PdM systems and workflows?
- What are the ethical issues in AI-based PdM?
- How can an efficient human–machine interaction in AI-based PdM systems be obtained?
- How can testing and validation of AI-based PdM systems be effectively conducted in real-world scenarios?
- What are recent advances and future trends in AI-based PdM?
2. Key Components in AI-Based Predictive Maintenance
- Sensors: Sensors are the frontline data collectors in a PdM system. These specialized devices are strategically placed on equipment and machinery to continuously monitor various parameters, such as temperature, pressure, vibration, and more. Sensor data provides real-time insights into equipment health and forms the foundation for predictive maintenance analysis.
- Data Preprocessing: Raw data obtained from sensors often contains noise and inconsistencies. Data preprocessing is the initial step in preparing the data for analysis. It includes data cleaning, normalization, and missing data handling. High-quality data are essential for accurate PdM modeling.
- AI Algorithms: AI algorithms, including machine learning and deep learning techniques, are the brain of the PdM systems. The algorithms analyze the data to identify the most important features relating to possible failures. They learn from historical data to predict equipment failures, anomalies, and RUL.
- Decision-Making Modules: The insights and predictions generated by the AI algorithms are processed by decision-making modules. These modules are responsible for determining when maintenance actions are needed. They can recommend preventive or corrective maintenance tasks, schedule maintenance, and trigger alerts to maintenance teams when necessary.
- Communication and Integration: Communication and integration ensure that the insights generated by the system are effectively translated into action. This component involves interactions with various stakeholders, including maintenance personnel and management. Furthermore, integration with enterprise systems such as ERP and asset management software aligns predictive maintenance with broader organizational goals.
- User Interface and Reporting: To make these insights accessible to maintenance staff and decision makers, user interfaces and reporting tools are essential. The tools make it easier for users to understand complex data patterns and make informed decisions by providing data visualization, dashboard, and reporting capabilities. Data visualization tools and dashboards communicate data insights and forecast information to maintenance teams and decision makers. Visual aids help understand complex data patterns and make informed decisions.
3. State-of-the-Art Techniques for Predictive Maintenance
4. Transparency and Explainability in AI-Based Predictive Maintenance
5. Challenges and Limitations of Using AI for PdM Autonomy
6. Recent Advances and Future Trends in AI-Based PdM
- Integration of advanced machine learning algorithms;
- Edge and cloud computing for real-time analysis and data storing;
- Predictive analytics with big data;
- XAI for transparency;
- IoT sensor integration;
- Digital twin, AR, VR, MR, and extended versions.
- Big data and analytics are used to collect, analyze, and interpret large amounts of data.
- The exponential growth of cyber–physical systems of digital twins, AR, VR, XR, metaverse, and human-driven industrial metaverse solutions to both physical and virtual work environments allows smooth collaboration and communication between employees, machines/robots, and AI.
- Development of autonomous maintenance systems that are capable of self-diagnosis, decision making, and proactive interventions without human intervention.
- Evolving toward zero-touch maintenance operations where AI systems automate the maintenance process from detection to resolution.
- Extraction of actionable insight advancements in AI algorithms to predict failures and provide actionable insights and recommendations for optimal maintenance strategies.
- Integrating experiential learning and reinforcement learning techniques to improve AI models based on ongoing data and continuous feedback.
- Implementation of blockchain technology for data security to enhance the security and integrity of PdM data, ensuring trust and transparency.
- Development of trustworthy AI algorithms and human-centric AI interfaces for better collaboration between AI systems and human operators, facilitating seamless interaction and decision making.
- Development of energy-efficient AI-based PdM to minimize resource consumption while maintaining high prediction accuracy.
- Development of collaborative robots (cobots), IIoT, edge and cloud computing, and 5G/6G connectivity for next-step PdM autonomy and smart factory that can adjust to shifting circumstances and changing conditions and streamline manufacturing processes.
- Development of generative AI models to contribute to the above items. For example, they can provide failure warnings, present encompassing instructions for repair and replacement methodologies, achieve suggestions to optimize energy consumption and cut down the carbon footprint to human operators by simulating the real system and/or analyzing maintenance logs and sensor data, and facilitate better collaboration between automated systems and human operators through natural language communication in automated maintenance planning.
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Ref. | Name | Description |
---|---|---|
[132,133] | NASA Turbofan Dataset-CMAPSSD and CMAPSSD-2 | The turbofan engine degradation simulation dataset, generated with the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dynamical model. |
[134] | PHM 2008 Dataset | The degradation collected from aircraft engines derived from CMAPSSD. |
[135] | NASA Ames Milling Dataset | Acoustic emission, vibration, and motor current data collected under different experimental conditions for predicting the milling tool wear. |
[136] | NASA Bearing Dataset | Run-to-failure vibration data from 4 accelerometers in a shaft. |
[137] | Case Western Reserve University (CWRU) Bearing Dataset | Test rig operating with different load conditions. |
[138] | FEMTO Ball Bearing Dataset from IEEE PHM Challenge | Run-to-failure temperature and vibration data from engine thermocouple and accelerometer sensors. |
[139] | Roll Bearing Dataset from IEEE PHM Challenge | A training set of six rolling bearings operated in three different conditions, and a testing set of 11 more. |
[140] | Backblaze Hard Disk Drive Dataset | The daily status of hard disk drives (HDDs), consisting of 433 failed drives and 22,962 good drives. |
[141] | PAKDD2020 Alibaba AI OPS Competition Dataset | HDD daily health status data including both a raw and a normalized value as well as a label and the time of failure. |
[142] | NASA Ames Prognostics Dataset | Li-ion battery degradation data during repeated charge and discharge cycles. |
[143] | Lithium-ion Battery Dataset of the University of Maryland | The current and voltage data on different EV drive cycles at varying ambient temperatures (including 0 °C, 25 °C, and 45 °C). |
[144] | MOSFET Thermal Overstress Aging Dataset | Run-to-failure experiments on power MOSFETs under thermal overstress. |
[145] | MAFAULDA | Fault measurements from machinery simulators run under different load conditions. |
[146] | Microsoft Azure PdM Dataset | Data modules of machines, telemetry, errors, maintenance, and failures collected by a Microsoft employee for PdM modeling collection. |
[147] | Global Energy Forecasting Competition (GEFCOM) Dataset | Hourly solar power generation data and assigning numerical weather forecasts from 1 April 2012 to 1 July 2014. |
[148] | The UCI SECOM Dataset | Measurements of features of semiconductor production within a semiconductor manufacturing process. |
Ref. | Class of Repair and Maintenance | Industry | Application | Problem Type/ Approach | Algorithm/Technology |
---|---|---|---|---|---|
[53] | Electrical equipment | Electrical and electronics | Aging monitoring for twisted pair specimens in low-voltage stator windings of electrical machines | Regression | 2D-CNN |
[54] | Computers and communication equipment | Information technology | RUL estimation on Microsoft Azure AI-based PdM dataset [146] | Regression | CNN, RNN, LSTM, CNN-LSTM, regression random forest (RRF), deep feed-forward (DFF) networks, and gated recurrent unit (GRU) |
[55] | Electrical equipment Electronic and optical equipment | Electrical and electronics Metals and plastics | Failure prediction on PAKDD2020 Alibaba AI OPS competition [141] and NASA bearing dataset [136] | Classification | CNN and time-series encoding techniques |
[56] | Electrical equipment | Metals and plastics | Health monitoring on NASA milling dataset [135] | Regression | 1D CNN |
[57] | Electrical equipment | Metals and plastics | RUL estimation on FEMTO bearing dataset [138] | Regression | LSTM and autoencoder |
[58] | Electrical equipment | Diagnosis and classification of faults in rotating machinery using MAFAULDA dataset [145] and CWRU-bearing datasets [137] | Classification | CNN | |
[59] | Electrical equipment | Metals and plastics | Fault diagnosis on the experimental data collected from a rotor fault diagnosis experimental platform and the CWRU bearing dataset [137] | Regression Classification | ELM, CNN, and autoencoder |
[60] | Electrical equipment | Electric vehicles battery technology | Charge estimation of lithium-ion battery state in electric vehicles | - | Bidirectional GRU circuit module, and attention circuit module |
[61] | Electronic and optical equipment | Electrical and electronics | The condition of rotating machinery in university laboratory by using a single-axis piezoelectric accelerometer | Classification | CNN |
[62] | Transport equipment | High-speed railway | Predictive and proactive maintenance for modeling physical degradation and failure in gas-insulated switchgear in high-speed railway | Regression | LSTM-RNN |
[63] | Electrical equipment | Electrical and electronics | RUL estimation of lithium-ion batteries in NASA Ames prognostics data repository [142] | Regression | LSTM |
[64] | Machinery | Aircraft manufacturing | Health condition in a horizontal machining center in an aircraft manufacturing cooperation | Regression | An attribute attentional LSTM |
[65] | Electronic and optical equipment | Medical devices and healthcare services | Failure diagnosis for the Vitros immunoassay analyzer in a local hospital in the United Arab Emirates by using IoT sensors | Classification | SVM |
[66] | Electronic and optical equipment | Nuclear power | Condition monitoring of nuclear infrastructure on NASA turbofan dataset [132,133] | Regression | SVM and logistic regression |
[67] | Electronic and optical equipment | Electrical and electronics | RUL estimation on MOSFET thermal overstress aging dataset [144] | Regression | SVM |
[68] | Buildings and other structures | Architecture, engineering, construction, and facility management | Data-driven condition monitoring based on building information modeling and IoT to predict the future condition of the mechanical, electrical, and plumbing components | Regression | FNN and SVM |
[69] | Fabricated metal products | Large service management | Failure prediction on Backblaze dataset [140] | Classification | Decision tree-based machine learning method |
[70] | Other machinery and equipment | Renewable energy, wind energy | Fault prediction in wind turbines | Classification | RF, decision tree algorithms, DBSC, and statistical process control |
[71] | Electrical equipment | Electronics manufacturing | RUL estimation of the equipment in an industry manufacturer of memory modules (DRAM and SSD) | Regression | Combined statistical process control charts and RF, XGBoost, and LSTM |
[72] | Electrical equipment | Electrical and electronics | PdM and health monitoring on appropriate quality data collected in the form of product measurements or readings from various machines | Classification | Statistical process control and naïve Bayes |
[73] | Motor vehicles | Autonomous vehicles | Hierarchical component-based health monitoring system with fault detection, diagnosis, and prognosis on the CaRINA II autonomous vehicle platform and the CARLA simulator | Classification | Dynamic Bayesian network |
[74] | Other machinery and equipment | Manufacturing | State and failure prediction of rim welding machine in the process of creating vehicle rims from the iron plate in the assembly line using IoT-based sensors | Classification | Naïve Bayes and Markov chain |
[75] | Electrical equipment | Electrical and electronics | Failure prediction in an electrical motor | Classification | Bayesian network |
[76] | Transport equipment, except motor vehicles | Transport Infrastructure | Failure prediction for rail bridges on the rail network in Great Britain | - | Bayesian network |
[77] | Motor vehicles | Automotive | Anomaly detection for off-road vehicle maintenance | Classification | HMM, kNN and isolation forest, and autoencoders |
[78] | Motor vehicles | Automotive | The ecological PdM through condition monitoring of a bus with diesel engines taking temperature, humidity, pollutant emissions (NOx, CO2, HC, and PM), emitted noise, etc. | - | HMM |
[79] | Transport equipment | Energy and sustainability | RUL estimation of the machines on the PHM 2008 dataset [134] | Regression | Cluster-based HMM |
[80] | Machinery | Semiconductor manufacturing | Condition monitoring in a semiconductor manufacturing station | Clustering | HMM |
[81] | Transport equipment | Energy and sustainability | RUL estimation using degradation indicators in an airplane engine on NASA turbofan engine dataset [132,133] | Unsupervised Learning | HMM |
[82] | Industrial machinery and equipment | Pumping systems | Fault detection and life cycle cost analysis of pumping systems | Statistics Regression Classification | SVM and HMM |
[83] | Transport equipment | Marine | PdM for cost estimation during the design process of a ship engine room | - | Bayesian probabilistic inferential approach and HMM |
[84] | Other machinery and equipment | Pharmaceutical manufacturing | Real-time health monitoring in an industrial freeze-dryer | Clustering, Classification | DBSC, K-means and GMMs, PCA, one-class SVM, and the local outlier factor |
[85] | Fabricated metal products | Cloud services and data centers | The detection of imminent hard disk drive failures in Backblaze dataset [140] | Classification | Apache Spark, which is an in-memory distributed data analysis platform, and RF |
[86] | Electronic and optical equipment | Smart manufacturing | A manufacturing big data ecosystem addressing the issues of big data ingestion, management, and analytics for fault/anomaly detection in IoT-based smart factories | Clustering | The distributed K-means clustering, MapReduce-based distributed PCA-based T-squared, and SPE algorithms A data lake, NoSQL database, and encryption protocol on the Apache Spark platform |
[87] | Transport equipment | Transport | PdM approach for malfunction evaluation in relation to the kilometers of the train and the periodicity of faults in the Greek Railway Company | Regression Classification | Classification trees J48 and regression trees M5 form algorithms |
[88] | Machinery and equipment | Electronics | Failure prediction of the monitored manufacturing industrial machinery by UCI SECOM dataset [148] | Classification | A fusion of data mining and semantics |
[89] | Other machinery and equipment | Textile | Production quality prediction in the textile industry | Regression | Supervisory control and data acquisition (SCADA) architecture to develop a cloud-based analytics module |
[90] | Transport equipment, except motor vehicles | Logistics and parcel delivery | A big data analytics framework for the data-driven prediction of courier package breaks in smart goods transportation systems | Classification | IoT networks Gradient boosting classifiers, SVM, logistic regression, and Apache Spark |
[91] | - | Vinyl flooring | Quality management in the vinyl flooring industry | - | Big data analytics and optimization Edge computing |
[92] | Electrical equipment | Industrial robots | Health status degradation assessment using real data of the ABB IRB 6400r industrial robot | Regression | Programmable logic controllers (PLC) One-class novelty detection using SVM and extreme learning machine (ELM) IIoT |
[93] | Electrical equipment | Metal and metallurgy | Fault detection in the friction stir welding tool | Classification | Best first tree classifier |
[94] | Electrical equipment | Electrical and electronics | Detection of the essence of the unbalanced conditions in the rotary machine in the constructed experimental setup | Classification | SVM |
[95] | Electrical equipment | Building and construction | Failure prediction in HVAC installations at a sports facility in a building automation system in the Paris region using sensors such as vibration, temperature, and energy consumption meters | Regression | LSTM and autoencoders IoT |
[96] | Electrical equipment | Manufacturing | Prediction of gradual degradation of an impeller using the sensors such as vibration, gyroscope/accelerometer, rotational speed, temperature, pressure, ambient pressure, temperature, and humidity on an industrial radial fan | Regression | Linear regression, RFR, and symbolic regression |
[97] | Electrical equipment | Semiconductor | Vibration-related failure prediction on a dataset including machines, errors, maintenance, telemetry, and failures | Regression | Linear regression |
[98] | Electrical equipment | Electrical and electronics | Fault detection in rotating machinery by monitoring and visualizing vibrations using transformed raw data into images through a short-time Fourier transform or Mel-frequency cepstral coefficients spectrogram | Classification | CNNs |
[99] | Fabricated metal products | Nuclear energy | PdM in the research reactor by using core-cooling pump vibration signals | Classification | FNN |
[100] | Electrical equipment | Railways | Fault detection using thermal imaging in rail systems in Turkiye | Classification | Fuzzy |
[101] | Machinery | Railways | Locomotive maintenance in Sri Lanka Railways for the issues of premature axle bearing defects, suspension bearing conditions, diesel engine inspection, compressor inspection, weak thermal insulation detection, dynamic grid resister element inspection, water, air, fuel, and oil pipeline blocks, fuse contractors, resistors, relays, and loose electrical wires | - | Thermal imaging technology |
[102] | Electrical equipment | Medical | PdM for progressive deterioration processes and failure mechanisms of different medical equipment | - | Infrared thermal imaging |
[103] | Electrical equipment | Hydraulics | PdM for evaluating premature failures of hydraulic drive systems in a university laboratory using simulation software AMESim from SIEMENS LMS Imagine.Lab | - | Some numerical simulations using infrared thermography |
[104] | Fabricated metal products, machinery, and equipment | Gearbox manufacturing | PdM through failure prediction and analysis in the gearbox high-speed shaft bearing using temperature and vibrational sensors | Classification | Decision trees |
[105] | Electrical equipment | Automotive | Intelligent PdM control through the collected data from condition monitoring sensors of electrical monorail system | Classification | Rule-base intelligence system IIoT and AR |
[106] | Fabricated metal products | Electrical and electronics | Fault detection and remote monitoring system to control the status of professional refrigeration systems | Regression | Planned SVM Digital twin, IoT, and MR |
[107] | Electrical equipment Electronic and optical equipment | Machine tools manufacturing | Fault prediction in machine tools equipped with various sensors to acquire huge volumes of production data in a typical machining workshop in Wuxi, China | Regression Reinforcement Learning | CNN, LSTM, and deep reinforcement learning (DRL) AR and IoT |
[108] | Fabricated metal products | Steel strip processing | Prediction of the real-time fatigue strength of the component under loading in steel strip processing lines | Regression | Finite element analysis, linear regression AR and IoT |
[109] | - | - | PdM system by a digital intelligent assistant for industry | - | NLP and user feedback about the success of maintenance interventions |
[110] | Electrical equipment | Automotive manufacturing | Prescriptive maintenance for evaluating failure and quality effects in an international manufacturer of gearboxes and engines for the automotive sector | - | Data management, predictive data analytic toolbox, recommender and decision support dashboard, and semantic-based learning and reasoning |
[111] | Fabricated metal products | Chemical industry | The prediction of failure time and probability of a pump | Classification | Ensembles of SVMs |
[112] | Electrical equipment | Manufacturing | The prediction of potential equipment failure, expected failure time, and expected repair time and providing the appropriate action for production planning and control in future factories | Reinforcement Learning | RL Digital twin |
[113] | Machinery | Manufacturing | RUL estimation in a machine park consisting of 100 machines | Reinforcement Learning | DRL |
[114] | Machinery | Rail transport | The optimal maintenance strategy jointly incorporates the effect of aging and degradation for locomotive wheelsets | - | Reliability analysis, sensitivity analysis, and a continuous stochastic process |
[115] | Transport equipment | Aviation | Discrete-event simulation framework for post-prognostic decision for aircraft maintenance using tire pressure indication system for an Airbus A320 | - | The technological maturity of an underlying PHM system |
[116] | Electrical equipment | Manufacturing | Anomaly detection in all the equipment in a global manufacturing system | Unsupervised Learning | Autoencoder-based deep learning technique Edge computing and IoT |
[117] | Machinery | Manufacturing | Failure prediction using different sensors such as temperature, rotation speed, vibration, and humidity in a ball-bearing automatic line | Regression | Autoregressive Integrated moving average model (ARIMA), ARIMA-LSTMs Traditional cloud-edge architecture |
[118] | Machinery | Industrial robotics manufacturing | Failure prediction for monitoring the health status of all machines in COMAU industrial robots company | Regression Classification | NN, RF, logistic regression, SVM, and gradient-boosted tree A hybrid cloud-edge computing |
[119] | Machinery | Air conditioning manufacturing | Failure prediction in air-conditioning systems | Classification | Centroid distance weighted federated averaging algorithm |
[120] | Electronic and optical equipment | Industry 4.0 | Blockchain framework for PdM in Industry 4.0 | Classification | Fuzzy logic, blockchain, case-based reasoning, and KNN |
[121] | Electrical equipment | Home energy management | Failure prediction of the applications in a home energy management system | Classification | IoT sensors SVM |
[122] | Electrical equipment | Electric vehicles battery technology | Prediction of starter battery failure times from a fleet of vehicles | - | Maximum likelihood approach |
[123] | Electrical equipment Machinery | Hydraulics | Energy-based maintenance for lubricant condition monitoring in a rubber-mixing hydraulic control system | Regression Classification | SVM and RF |
[124] | Machinery | Steel manufacturing | RUL estimation in hot rolling milling machines regarding segment surface temperatures and hydraulic force measurements | - | Maximum likelihood approach |
[125] | Fabricated metal products Machinery and equipment | Robotics manufacturing | Failure prediction in a discrete multi-robot mobile assembling line using sensors such as energy analyzer modules, temperature, vibration, corrosion, and humidity | Classification | FNN |
[126] | Machinery and equipment | Electrical and electronics | PHM-based PdM for the degradation estimation of gear motor assembly in mechanical power transmission | - | Cloud computing and the multitenancy principle |
[127] | Electrical equipment Electronic and optical equipment | Electrical and electronics | The detection of changes in the operating conditions and abrupt faults in the platform composed of an asynchronous motor and a gearbox made of two pulleys in the university laboratory | - | Edge and cloud analytics |
[128] | - | Manufacturing | Product lifecycle management by connecting the industrial unit floor with design and manufacturing engineers | - | A predictive analytics software platform |
Class of Repair and Maintenance | Study Number | Industry | Study Number | PdM Task | Study Number | Problem Type/ Approach | Study Number |
---|---|---|---|---|---|---|---|
Electrical equipment | 30 | Electrical and electronics; electronics | 13 | Failure prediction | 22 | Classification | 33 |
Machinery | 10 | Manufacturing; manufacturing of automotive manufacturing; aircraft semiconductor; machine tools; gearbox; smart; pharmaceutical; electronics; steel; robotics; industrial robotics; air conditioning; industrial robots; Industry 4.0 | 22 | RUL estimation; Cost and charge estimation | 11 | Regression | 26 |
Electronic and optical equipment | 9 | Transport; automotive; high-speed railway; autonomous vehicles; railways; rail transport | 10 | Fault detection | 12 | Clustering | 3 |
Fabricated metal products | 8 | Metals and plastics; metal and metallurgy | 5 | Condition monitoring; Vibration monitoring | 9 | Reinforcement Learning | 3 |
Transport equipment | 6 | Energy and sustainability; nuclear power; nuclear energy; renewable energy; wind energy | 5 | Anomaly detection | 4 | Unsupervised Learning | 2 |
Other machinery and equipment | 5 | Information technology; cloud services and data centers | 2 | Production quality prediction | 1 | The others | 16 |
Motor vehicles | 3 | Electric vehicle battery technology | 2 | Product lifecycle management | 1 | ||
Machinery and equipment | 3 | Medical; medical devices and healthcare services | 2 | Component fatigue strength prediction | 1 | ||
Transport equipment, except motor vehicles | 2 | Building and construction; architecture; engineering; construction; facility management | 2 | PdM system by a digital intelligent assistant | 1 | ||
Computers and communication equipment | 1 | Hydraulics | 2 | Blockchain framework for PdM | 1 | ||
Industrial machinery and equipment | 1 | Large service management | 1 | A big data analytics framework | 1 | ||
Buildings and other structures | 1 | Infrastructure; pumping systems; marine; textile; logistics and parcel delivery; steel strip processing; chemical industry; aviation; home energy management | 9 | Post-prognostic decision | 1 |
Techniques | Accuracy | Cost-Effectiveness | Scalability |
---|---|---|---|
Deep Learning | High | Moderate | High |
Machine Learning | Moderate | High | Moderate |
Methods | Accuracy | Cost-Effectiveness | Scalability |
---|---|---|---|
AI-based | High | Moderate | High |
Traditional | Moderate | High | Low |
Ref. | The Approach | Application |
---|---|---|
[174] | SHAP | PdM in a coal crusher operating at the boiler of the real power plant and gantries in a steelworks converter, a transport line in a steelworks converter |
[175] | SHAP | PdM in a coal crusher operating presented in [174] |
[176] | LIME, SHAP, and ELI5 | Solar photovoltaic energy forecasting by GEFCOM dataset [147] |
[177] | Integrated gradients, SHAP, and SmoothGrad | Anomaly detection in press machine data of a production line |
[178] | SHAP and LIME. | RUL estimation of hard disk drive in the Backblaze dataset [140] |
[179] | SHAP | RUL estimation of the engines on the NASA turbofan engine dataset [132,133] |
[180] | Integrated gradients | PdM to reveal the most sensitive gearbox operations in the MW load range in a petrochemical plant to a specific abnormality |
[181] | SHAP | RUL estimation of NASA turbofan engine dataset [132,133] |
[182] | LIME and NLP | Maintenance work orders |
[183] | LRP | Bearing health condition estimation on the NASA bearing dataset [136] |
[184] | Knowledge-based system including domain ontologies and semantic web rule language rules | The detection of future machinery failures as well as the prediction of their time of occurrence in semiconductor manufacturing process by the UCI SECOM dataset [148] |
[185] | Rule-based expert system | PdM of a real hybrid bus |
[186] | Real type-2 fuzzy-based XAI | PdM within the water pumping industry |
[187] | Rule-based model called logic language model | RUL estimation of the engines on the NASA turbofan engine dataset [132,133] |
[188] | QARM algorithm | RUL prediction of a drilling machine of an automotive manufacturer |
[189] | A data-driven sensitivity analysis | The prediction of the future reliability of components in a large gas distribution network |
[190] | Bagged decision trees | A synthetic dataset that reflects real predictive maintenance data encountered in the industry |
[191] | Gradient boosting decision tree | The prediction machine errors or tool failures on Microsoft Azure dataset [146] |
[192] | A premier transparent, interpretable, and self-explainable automated machine learning software, including methods like random forest and gradient boosting | Manufacturing quality prediction in real-life environment |
[193] | A virtual knowledge graph-based approach | PdM in the hydraulic systems |
[194] | Attention and LSTM-GAN | PdM to reduce maintenance costs and downtime of machines in the intelligent manufacturing system |
[195] | Attention and bidirectional LSTM | RUL estimation on the NASA turbofan engine dataset [132,133] |
[196] | Bidirectional self-attention gated recurrent unit | The prediction of the health index on the NASA rolling bearing dataset from IEEE PHM challenge [138] |
[197] | The attention mechanism | RUL estimation on the NASA turbofan engine dataset [132,133] |
[198] | The attention mechanism | Structural Health Monitoring |
[199] | The multi-layer, multi-source attention distribution | The fault detection and recognition on the general data hierarchy of AUV |
[200] | Attention and LSTM | RUL estimation on the NASA turbofan engine dataset [132,133] IoT |
[201] | Feature attribution and counterfactual generation | Fault diagnosis in water injection pump for production stimulation in offshore oil wells, offshore natural gas treatment plant |
[202] | PCA/Kernel PCA/KNN-PCA, LIME, and integrated gradient | RUL estimation of the NASA turbofan engine dataset [132,133] |
[203] | Pobability density function, Fourier transform, spectral kurtosis, autoencoder and variational autoencoder, and K-means clustering | Gearbox and bearing health assessment in wind turbine system |
[204] | Unsupervised feature selection, adapting relevance metrics with the dynamic time-warping algorithm | Health indicators for a rotating machine |
[205] | Temporal fusion separable convolutional network, a hierarchical latent space variational auto-encoder, and a regressor consisting of a linear layer and a sigmoid activation function | RUL estimation on the NASA turbofan engine dataset [132,133] |
[206] | A blockchain-based architecture that achieves trustworthy federated learning | A service |
[207] | Balanced K-star | PdM in an IoT-based manufacturing system |
[208] | HMM and reinforcement learning | RUL estimation of the engines on the NASA turbofan engine dataset [132,133] |
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Ucar, A.; Karakose, M.; Kırımça, N. Artificial Intelligence for Predictive Maintenance Applications: Key Components, Trustworthiness, and Future Trends. Appl. Sci. 2024, 14, 898. https://doi.org/10.3390/app14020898
Ucar A, Karakose M, Kırımça N. Artificial Intelligence for Predictive Maintenance Applications: Key Components, Trustworthiness, and Future Trends. Applied Sciences. 2024; 14(2):898. https://doi.org/10.3390/app14020898
Chicago/Turabian StyleUcar, Aysegul, Mehmet Karakose, and Necim Kırımça. 2024. "Artificial Intelligence for Predictive Maintenance Applications: Key Components, Trustworthiness, and Future Trends" Applied Sciences 14, no. 2: 898. https://doi.org/10.3390/app14020898