## 4 Quality Control
### Anomaly Detection  
The other significant quality inspection category is anomaly detection. The goal of anomaly detection--also referred to as outlier detection--is to determine all instances dissimilar to the others or the required instances. Ref. [115] defines an outlier as an observation that deviates so significantly from other observations as to arouse suspicion that a different mechanism generated it. Ref. [116] introduces an architecture where the machine learning algorithm can detect defective bearings and continually tunes the quality testing process parameters. Specifically, the identification of defective bearings is performed using a voting classifier fed by statistical metrics measured from the collected experiments. The paper evaluates several machine learning methods, including k-neighbors, SVC, Decision Tree, Random Forest, Multi-Layer Perceptron, AdaBoost, Naive Bayes, Gradient Boost, and Voting Classifier methods. Ref. [117] proposes a method in which (1) the manufacturing processes classification is performed using the Support Vector Machine (SVM) algorithm, (2) the regularization parameter value and the gamma coefficient value of the SVM algorithm are optimized using the Horse Optimization Algorithm (HOA), (3) the HOA -based SVM results are compared to Particle Swarm Optimization (PSO)-based SVM results and Chicken Swarm Optimization (CSO)-based SVM results. Additionally, ref. [118] uses PSO and DNN for a similar problem. Both methods are validated on SEMCOM dataset [119].  
Figure 5: General architecture model for vision-based product quality inspection [104].  
Without application dependability or a case study, ref. [120] introduces an anomaly detection method based on the Gaussian Restricted Boltzmann Machine for industry product quality inspection. Ref. [121] addresses the critical issues of machine learning-based condition monitoring solutions. Ref. [122] studies several unsupervised learning techniques (Gaussian model, SVM, isolation forest, autoencoder) based on six industrial test datasets. Ref. [123] focuses on a particular application of telemetry--anomaly detection on time-series data. It presents an improved version of ReRe, a state-of-the-art Long Short Term Memory-based machine learning algorithm.  
A fuzzy neural network-based fault diagnosis approach for condition monitoring is presented by [124] for rotating machines via vibration signals. Ref. [125] proposes a long short-term memory (LSTM)-Gauss-NBayes method for outlier detection in the IIoT. LSTM-NN builds a model on a normal time series and it detects outliers by utilizing the predictive error for the Gaussian Naive Bayes model. Ref. [126] proposes an on-device federated learning-based deep anomaly detection framework for sensing time-series data in IIoT. The framework used an attention mechanism-based convolutional neural network-long short-term memory (AMCNN-LSTM) model to detect anomalies accurately. Similarly, ref. [127] proposes a reliable anomaly detection strategy for IIoT using federated learning. Specifically, it applies the federated learning technique to build a universal anomaly detection model with each local model trained by the deep reinforcement learning algorithm.  
In contrast, ref. [128] introduces a practical investigation on graph neural networks (GNNs) for anomaly detection in IIoT-enabled smart transportation, smart energy, and smart factory. Ref. [127] designs an anomaly detection algorithm that exploits deep learning techniques to assess the working conditions of the plant. AE and deepAE are responsible for initial dimensionality reduction, PCA is responsible for a further reduction, and K-means clustering performs the actual anomaly detection. Ref. [129] develops a methodology for detecting abnormal behavior in the context of aging IIoT using a PCA-based method.