Abstract
The past decade has witnessed the adoption of artificial intelligence (AI) in various applications. It is of no exception in the area of prognostics and health management (PHM) where the capacity of AI has been highlighted through numerous studies. In this paper, we present a comprehensive review of AI-based solutions in engineering PHM. This review serves as a guideline for researchers and practitioners with varying levels of experience seeking to broaden their know-how about AI-based PHM. Specifically, we provide both a broad quantitative analysis and a comprehensive qualitative examination of the roles of AI in PHM. The quantitative analysis offers an insight into the research community’s interest in AI-based approaches, focusing on the evolution of research trends and their developments in different PHM application areas. The qualitative survey gives a complete picture on the employment of AI in each stage of the PHM process, from data preparation to decision support. Based on the strengths and weaknesses of existing methods, we derive a general guideline for choosing proper techniques for each specific PHM task, aiming to level up maintenance practitioners’ efficiency in implementing PHM solutions. Finally, the review discusses challenges and future research directions in the development of autonomous intelligent PHM solutions.
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The extracted and curated data used in this paper are available upon request to the corresponding author.
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Khanh Nguyen: conceptualization, methodology, software, validation, formal analysis, investigation, writing of original draft, visualization, review and editing; Kamal Medjaher: conceptualization, methodology, writing of original draft, visualization, review and editing; Do Tran: conceptualization, data curation, software, writing of original draft, review and editing.
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Annex
Annex
Search string used for this review:
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To create the list of unsuitable categories According to the WC (Web of Science category) column and the ID (keyword plus) column, we extract the unique categories/keywords from their correspondent column and sort them with their occurrence frequency. Then, by investigating the obtained results, we identify the unsuitable categories/keywords according to our knowledge and record them in the particular lists.
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Examples of unsuitable categories: ‘audiology & speech-language pathology’, ‘ecology’, ‘agriculture, dairy & animal science’, ‘endocrinology & metabolism’, ‘oncology’, ‘genetics & heredity’, ‘biodiversity conservation’, ‘psychiatry’, ‘cardiac & cardiovascular systems’, ‘geography, physical’, ‘business, finance’, ‘neuroimaging’, ‘transplantation’, ‘toxicology’, ‘geochemistry & geophysics’, ‘urban studies’, ‘evolutionary biology’, ‘microscopy’, ‘critical care medicine’, ‘regional & urban planning’, ‘microbiology’, ‘physiology’, ‘cell & tissue engineering’, ‘psychology’, ‘forestry’, ‘surgery’, ‘dentistry, oral surgery & medicine’, ‘social sciences, ‘psychology, experimental’, ‘political science’, ‘medicine, general & internal’, ‘public & occupational health’, ‘information science & library science’, ‘pharmacology & pharmacy’, ‘agricultural economics & policy’, ‘social issues’, ‘medical laboratory technology’, ‘cell biology’, ‘rehabilitation’, ‘behavioral sciences’, ‘nursing’, ‘orthopedics’, ‘communication’, ‘horticulture’, ‘immunology’, ‘limnology’, ‘health care sciences & services’.
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Examples of inappropriate keywords: ‘cancer’, ‘cells’, ‘gene-expression’, ‘carcinoma’, ‘apoptosis’, ‘biomarkers’, ‘bispectrum’, ‘brain’, ‘in-vitro’, ‘in-vivo’, ‘adenocarcinoma’, ‘angiogenesis’, ‘hepatocellular-carcinoma’, ‘lung-cancer’, ‘squamous-cell carcinoma’, ‘colorectal-cancer’, ‘ovarian-cancer’, ‘tumor’, ‘tumorigenesis’, ‘cancer-cells’, ‘prostate-cancer’, ‘lesions’, ‘dementia’, ‘alzheimers-disease’, ‘adhesion’, ‘cell lung-cancer’, ‘colon-cancer’, ‘adults’, ‘methylation’, ‘prevalence’, ‘myocardial-infarction’, ‘estrogen’, ‘immunohistochemistry’, ‘micrornas’, ‘gliomas’, ‘breast-cancer diagnosis’, ‘high-grade gliomas’,’drug-resistance’, ‘blood-pressure’, ‘pathology’, ‘sepsis’, ‘malignant gliomas’, ‘microrna-related genes’, ‘mesenchymal transition’, ‘culture’, ‘monoclonal-antibodies’, ‘ubiquitination’, ‘heart-rate-variability’, ‘genome’, ‘uio sequences’, ‘polymorphisms’, ‘bispectra’, ‘bioinformatics’, ‘chirplet transform’, ‘biogenesis’, ‘protein’, ‘metastases’, ‘chromatin’, ‘gene-expression’, ‘skeletal-muscle’, ‘lung’, ‘mice’, ‘e-cadherin’,’beta-catenin’, ‘antibodies’.
To create the list of AI-related keywords We identify the appropriate keyworks from the corresponding list (extracted from ID column) according to our knowledge and record them in a particular list.
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Examples of AI-related keywords: ‘machine learning’, ‘supervised learning’, ‘unsupervised learning’, ‘semi supervised learning’, ‘reinforcement learning’, ‘evolutionary’, ‘genetic algorithm’, ‘genetic programming’, ‘fuzzy inference system’, ‘clustering’, ‘classification’, ‘particle swarm’, ‘deep learning’,‘recurrent network’, ‘convolutional network’, ‘recurrent-network’, ‘convolutional-network’,‘deep belief’, ‘bayesian network’, ‘auto encoder’, ‘neural network’, ‘neuro fuzzy’ ‘svm’, ‘self organizing map’, ‘self-organizing map’ ‘decision tree’, ‘anfis’, ‘artificial intelligence’, ‘ant colony optimization’, ‘gradient boosting regression’ ‘statistical learning’, ‘partial least square’, ‘isomap’, ‘radial basis function’, ‘organizing map’, ‘neighbour nearest’, ‘pca’, ‘knn’, ‘lle’, ‘ lda ’, ‘ica’, ‘fda’, ‘ann’, ‘cnn’, ‘lstm’, ‘rnn’, ‘rbm’, ‘gru’, ‘narx’, ‘som’, ‘neural-network’, ‘extreme learning-machine’, ‘pattern-recognition’, ‘fisher discriminant’, ‘particle swarm optimization’, ‘rbfn’, ‘classifier’, ‘vector machine’, ‘autoencoder’, ‘random forest’, ‘artificial-intelligence’, ‘learning-method’, ‘restricted boltzmann’, ‘pattern recognition’, ‘extreme learning’, ‘t-sne’, ‘umap’,‘regression’.
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Nguyen, K.T.P., Medjaher, K. & Tran, D.T. A review of artificial intelligence methods for engineering prognostics and health management with implementation guidelines. Artif Intell Rev 56, 3659–3709 (2023). https://doi.org/10.1007/s10462-022-10260-y
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DOI: https://doi.org/10.1007/s10462-022-10260-y