This survey explores applications of explainable artificial intelligence in manufacturing and industrial cyber–physical systems. As technological advancements continue to integrate artificial intelligence into critical infrastructure and industrial processes, the necessity for clear and understandable intelligent models becomes crucial. Explainable artificial intelligence techniques play a pivotal role in enhancing the trustworthiness and reliability of intelligent systems applied to industrial systems, ensuring human operators can comprehend and validate the decisions made by these intelligent systems. This review paper begins by highlighting the imperative need for explainable artificial intelligence, and, subsequently, classifies explainable artificial intelligence techniques systematically. The paper then investigates diverse explainable artificial-intelligence-related works within a wide range of industrial applications, such as predictive maintenance, cyber-security, fault detection and diagnosis, process control, product development, inventory management, and product quality. The study contributes to a comprehensive understanding of the diverse strategies and methodologies employed in integrating explainable artificial intelligence within industrial contexts.

There has been an increasing interest in the design of intelligent diagnostic systems for industrial applications. The key requirement in the design of practical diagnostic systems is the ability for decision making in high-dimensional feature spaces, where the prior knowledge about the system states in terms of labels is very limited. Moreover, the problem of diagnosing simultaneous defects is rarely addressed on real industrial applications. This work aims to develop a semi-supervised deep-learning scheme for diagnosing multiple defects including simultaneous ones in a gearbox directly connected to an induction machine shaft. This scheme consists of two main modules: information fusion and decision making. The former integrates captured multiple sensory streams into a very high dimensional feature space. The latter uses a semi-supervised deep learning procedure to minimize the human interaction during the training and maximize the diagnostic efficiency. This scheme facilitates learning and diagnosing defects under harshest conditions (i) where only a few number of labeled samples are collected together with a large number of unlabeled samples, and (ii) in a very high-dimensional feature space. Several state-of-the-art semi-supervised and supervised learners have also been included in the scheme, enabling a comparative experiment for diagnosing simultaneous defects.

This paper focuses on the development of an integrated scheme for diagnosing bearing defects in induction motors, under the class-imbalanced condition. This scheme comprises of four main modules: segmentation, feature extraction, feature reduction, and fault classification. Various state-of-the-art techniques have been devised in the feature extraction and reduction modules to extract informative sets of features from a raw vibration signal, filter redundant features, and produce the most distinct features for the following module. The fault classification module adapts various state-of-the-art class-imbalanced learning techniques for diagnosing bearing defects. This module contains a novel imputation-based oversampling technique for class-imbalanced learning. This integrated diagnostic scheme is evaluated on three experimental scenarios with different imbalance ratios. The reasonable diagnostic performances confirm the ability of the proposed novel class-imbalanced learning technique in diagnosing bearing defects, independently from the imbalance ratios.

This paper proposes a novel adversarial scheme for learning from data under harsh learning conditions of partially labelled samples and skewed class distributions. This novel scheme integrates the generative ability of the state-of-the-art conditional generative adversarial network with the semi-supervised deep ladder network and semi-supervised deep auto-encoder. The proposed generative-adversarial based semi-supervised learning framework, named GBSS, is a triple network that aims to optimize a newly defined objective function to enhance the performance of the semi-supervised learner with the help of a generator and discriminator. The duel between the generator and discriminator results in the generation of more synthetic minority class samples that are very similar to the original minority samples (attacks and faults). Meanwhile, GBSS trains the semi-supervised model to learn the general distribution of the minority class samples including the newly generated samples in contrast to other classes and iteratively adjusts its weights. Moreover, a diagnostic framework is designed, in which GBSS and several state-of-the-art semi-supervised learners are used for learning and diagnosing attacks and faults in power grids. These methods are evaluated and compared for diagnosing attacks and faults in two different power grid cases. The attained results demonstrate the superiority of GBSS in diagnosing attacks and faults under the harsh conditions.

Design of the data-driven diagnostic systems usually requires to have labeled data during the training session. This paper aims to design a hybrid data-driven framework for diagnosing faults, where the data labels are not available to a large extent. This hybrid framework has five steps for transforming raw vibration signals to informative sets of samples for decision making. It uses several state-of-the-art approaches for feature extraction and semi-supervised feature reduction. The decision making step uses a number of state-of-the-art semi-supervised learners. This step also comprises a novel surface estimation approach that is developed for semi-supervised learning. The proposed hybrid framework is applied for diagnosing bearing defects in induction motors and validated based on four scenarios, each of which is experimented with different amounts of labeled samples. The attained diagnostic accuracies show the efficiency of the proposed hybrid framework including the novel semi-supervised learner in classifying bearing defects, regardless of the number of labeled samples.

While the quality of the synchronized measurements is of paramount importance for real-time monitoring and protection of the power grids, collected measurements often contain missing values. This paper proposes a scheme for diagnosing attacks and faults in the presence of missing measurements in power grid data. The proposed scheme contains four modules for clustering, missing data imputation, decision-making, and optimization. This paper develops a novel technique for missing data imputation based on the correlation-connected clusters that consider local correlation among the measurements in estimating missing data, handle high-dimensional data, and tolerate high missing ratios. The optimization module ties the imputation process to diagnostic performance. The proposed novel imputation technique is compared with other state-of-the-art techniques within the diagnostic scheme. The achieved results show that the proposed technique significantly outperforms other competitors.