# Condition-Based Monitoring and Maintenance: State of the Art Review
## 2 Literature Review
### Artificial Intelligence  
Since failures involve uncertainties/non-stationary processes, various artificial techniques (i.e., neural network, genetic algorithm, fuzzy logics, expert systems) are frequently used for condition assessment and decision making. Applications of artificial intelligence to CBM to widen the scope of expert systems and to use it for machine diagnosis have been presented [65], and when administered properly through AI, can prevent accidents and increase the resale value of machines. Hasan et al. [66] proposed an explainable AI-based approach for bearing fault diagnosis under variable speed and load conditions. A five-stage scheme is suggested to identify faults in the observed bearing signals: fast discrete orthogonal Stockwell transformation (FDOST) wrapper-based feature selector--Boruta, the Spearman's rank correlation coefficient (SRCC), the k-nearest neighbor (k-NN) algorithm, and the Shapley additive explanation (SHAP) for model interpretation. Garcia et al. [67] provided the SIMAP Intelligent System for predictive maintenance to the health condition monitoring of a wind-turbine gearbox by taking account the information coming in real time from different sensors and other information sources. It is concluded that artificial intelligence and modelling techniques are adequate for reaching the main goals of the predictive maintenance strategy. Kothamasu et al. [68] presents an approach for incorporating population characteristic information and suspended condition trending data of historical units into prognosis using a feed-forward neural network, Kaplan-Meier estimator, and a degradation-based failure probability density function (PDF) estimator. The comparative model is used with a conventional time series prediction model to predict more accurately further ahead than similar methods that do not include population characteristics and/or suspended data in prognosis. In another combinational modeling (qualitative physics and product modeling) for product maintenance, model -based maintenance in terms of inspection, monitoring, diagnosis, and planning based on functional, behavioral, and state-of-the-art models are discussed. Effective integration on AI in CBM will enhance usability and predictability of the failure systems. Jiang et al. [69] proposed a novel method based on improved convolution bearing fault diagnosis using neural network and transfer learning. The transfer learning (TL)-based framework can save a significant amount of time in the course of task completion [70; 71]. This method was adopted by Hasan et al. [72] to identify bearing faults under certain fault sizes. Zheng et al. [73] used multi-synchro squeezing S-transform for fault diagnosis in rolling bearings. Genetic algorithms and fuzzy logic are used to model uncertainties/non-stationary processes. Since there are lot of uncertainties in the maintenance systems and sometimes the systems are not well structured, the fuzzy techniques would be more applicable for failure prediction and condition assessment [74; 75]. Optimizing maintenance and repair policies were presented with a combination of genetic algorithms and Monte Carlo simulation within the context of plant logistic management on the choice of maintenance and repair strategies for an industrial plant, in the face of reliability and economic constraints. Monte Carlo simulation was used for economic analysis [76]. In another study, Marseguerra et al. came up with a similar hybrid technique with stress-dependent degradation processes for load sharing components and of a reduced number of maintenance workers available on site [77]. Sharma uses an approach based on fuzzy linguistic modeling to select the most effective and efficient maintenance strategy with three input parameters (historical data, present data, and competence of data). Although it uses a fuzzy set and rule-based system, it may not provide a complete scenario [78]. A fuzzy multiple criteria decision-making (MCDM) evaluation methodology is used for the cost-effective maintenance approach [79].