Search Results (256)

Predictive maintenance in manufacturing industry applications is a challenging research field. Packaging machines are widely used in a large number of logistic companies’ warehouses and must be working uninterruptedly. Traditionally, preventive maintenance strategies have been carried out to improve the performance of these machines. However, these kinds of policies do not take into account the information provided by the sensors implemented in the machines. This paper presents an expert system for the automatic estimation of work orders to implement predictive maintenance policies for packaging machines. The central innovation lies in a two-stage process: a classifier generates a binary decision on whether a machine requires maintenance, and an unsupervised anomaly detection module subsequently audits the classifier’s probabilistic output to refine and interpret its predictions. By leveraging the classifier to condense sensor data and applying anomaly detection to its output, the system optimizes the decision reliability. Three anomaly detection methods were evaluated: One-Class Support Vector Machine (OCSVM), Minimum Covariance Determinant (MCD), and a majority (hard) voting ensemble of the two. All anomaly detection methods improved the baseline classifier’s performance, with the majority voting ensemble achieving the highest F1 score. Full article

In the face of growing Internet of Things (IoT) security challenges, traditional Intrusion Detection Systems (IDSs) fall short due to IoT devices’ unique characteristics and constraints. This paper presents an effective, lightweight detection model that strengthens IoT security by addressing the high dimensionality of IoT data. This model merges an asymmetric stacked autoencoder with a Deep Neural Network (DNN), applying one-class learning. It achieves a high detection rate with minimal false positives in a short time. Compared with state-of-the-art approaches based on the BoT-IoT dataset, it shows a higher detection rate of up to 96.27% in 0.27 s. Also, the model achieves an accuracy of 99.99%, precision of 99.21%, and f1 score of 97.69%. These results demonstrate the effectiveness and significance of the proposed model, confirming its potential for reliable deployment in real IoT security problems. Full article

Detecting malware has become extremely important with the increasing exposure of computational systems and mobile devices to online services. However, the rapidly evolving nature of malicious software makes this task particularly challenging. Despite the significant number of machine learning works for malware detection proposed in the last few years, limited interest has been devoted to continual learning approaches, which could allow models to showcase effective performance in challenging and dynamic scenarios while being computationally efficient. Moreover, most of the research works proposed thus far adopt a fully supervised setting, which relies on fully labelled data and appears to be impractical in a rapidly evolving malware landscape. In this paper, we address malware detection from a continual semi-supervised one-class learning perspective, which only requires normal/benign data and empowers models with a greater degree of flexibility, allowing them to detect multiple malware types with different morphology. Specifically, we assess the effectiveness of two replay strategies on anomaly detection models and analyze their performance in continual learning scenarios with three popular malware detection datasets (CIC-AndMal2017, CIC-MalMem-2022, and CIC-Evasive-PDFMal2022). Our evaluation shows that replay-based strategies can achieve competitive performance in terms of continual ROC-AUC with respect to the considered baselines and bring new perspectives and insights on this topic. Full article

Multi-class anomaly detection is more efficient and less resource-consuming in industrial anomaly detection scenes that involve multiple categories or exhibit large intra-class diversity. However, most industrial image anomaly detection methods are developed for one-class anomaly detection, which typically suffer significant performance drops in multi-class scenarios. Research specifically targeting multi-class anomaly detection remains relatively limited. In this work, we propose a powerful unified normalizing flow for multi-class anomaly detection, which we call UniFlow. A multi-cognitive visual adapter (Mona) is employed in our method as the feature adaptation layer to adapt image features for both the multi-class anomaly detection task and the normalizing flow model, facilitating the learning of general knowledge of normal images across multiple categories. We adopt multi-cognitive convolutional networks with high capacity to construct the coupling layers within the normalizing flow model for more effective multi-class distribution modeling. In addition, we employ a multi-scale feature fusion module to aggregate features from various levels, thereby obtaining fused features with enhanced expressive capabilities. UniFlow achieves a class-average image-level AUROC of 99.1% and a class-average pixel-level AUROC of 98.0% on MVTec AD, outperforming the SOTA multi-class anomaly detection methods. Extensive experiments on three benchmark datasets, MVTec AD, VisA, and BTAD, demonstrate the efficacy and superiority of our unified normalizing flow in multi-class anomaly detection. Full article

The usage of anomaly detection is of critical importance to numerous domains, including structural health monitoring (SHM). In this study, we examine an online setting for damage detection in the Z24 bridge. We evaluate and compare the performance of the elliptic envelope, incremental one-class support vector classification, local outlier factor, half-space trees, and entropy-guided envelopes. Our findings demonstrate that XGBoost exhibits enhanced performance in identifying a limited set of significant features. Additionally, we present a novel approach to manage drift through the application of entropy measures to structural state instances. The study is the first to assess the applicability of one-class classification for anomaly detection on the short-term structural health data of the Z24 bridge. Full article

The safe operation of aero-engines is crucial for ensuring flight safety, and effective fault detection methods are fundamental to achieving this objective. In this paper, we propose a novel approach that integrates an auto-encoder with long short-term memory (LSTM) networks and a self-attention mechanism for the anomaly detection of aero-engine time-series data. The dataset utilized in this study was simulated from real data and injected with fault information. A fault detection model is developed utilizing normal data samples for training and faulty data samples for testing. The LSTM auto-encoder processes the time-series data through an encoder–decoder architecture, extracting latent representations and reconstructing the original inputs. Furthermore, the self-attention mechanism captures long-range dependencies and significant features within the sequences, thereby enhancing the detection accuracy of the model. Comparative analyses with the traditional LSTM auto-encoder, as well as one-class support vector machines (OC-SVM) and isolation forests (IF), reveal that the experimental results substantiate the feasibility and effectiveness of the proposed method, highlighting its potential value in engineering applications. Full article

Idlers are essential to conveyor systems, as well as supporting and guiding belts to ensure production efficiency. Proper idler maintenance prevents failures, reduces downtime, cuts costs, and improves reliability. Most studies on idler fault detection rely on supervised methods, which depend on large labelled datasets for training. However, acquiring such labelled data is often challenging in industrial environments due to the rarity of faults and the labour-intensive nature of the labelling process. To address this, we propose the chroma-augmented semi-supervised anomaly detection (CASSAD) method, designed to perform effectively with limited labelled data. At the core of CASSAD is the one-class SVM (OC-SVM), a model specifically developed for anomaly detection in cases where labelled anomalies are scarce. We also compare CASSAD’s performance with other common models like the local outlier factor (LOF) and isolation forest (iForest), evaluating each with the area under the curve (AUC) to assess their ability to distinguish between normal and anomalous data. CASSAD introduces chroma features, such as chroma energy normalised statistics (CENS), the constant-Q transform (CQT), and the chroma short-time Fourier transform (STFT), enhanced through filtering to capture rich harmonic information from idler sounds. To reduce feature complexity, we utilize the mean and standard deviation (std) across chroma features. The dataset is further augmented using additive white Gaussian noise (AWGN). Testing on an industrial dataset of idler sounds, CASSAD achieved an AUC of 96% and an accuracy of 91%, surpassing a baseline autoencoder and other traditional models. These results demonstrate the model’s robustness in detecting anomalies with minimal dependence on labelled data, offering a practical solution for industries with limited labelled datasets. Full article

A harmonic reducer is an important component of industrial robots. In practical applications, it is difficult to obtain enough anomaly data from error cases for the supervised training of models. Whether the information contained in regular features is sensitive to anomaly detection is unknown. In this paper, we propose an anomaly detection frame for a harmonic reducer with only healthy data. We considered an auto-encoder trained using only healthy features, such as feature mapping, in which the difference between the output and the input constitutes a new high-dimensional feature space that retained information relevant only to anomalies. Compared to the original feature space, this space was more sensitive to abnormal data. The mapped features were then fed into the OCSVM to preserve the feature details of the abnormal information. The effectiveness of this method was validated by multiple sets of data collecting from harmonic reducers. Three different residual calculations and four different AE models were used, showing that the method outperforms an AE or an OCSVM alone. It is also verified that the method outperforms other typical anomaly detection methods. Full article

The analysis of fast fluorescence kinetics, specifically through the JIP test, is a valuable tool for identifying and characterizing plant stress. However, interpreting OJIP data requires a comprehensive understanding of their underlying theory. This study proposes a Machine Learning-based approach using a One-Class Support Vector Machine anomaly detection model to effectively categorize OJIP measurements into “normal”, representing healthy plants, and “anomalies”. This approach was validated using a previously published dataset. A subgroup of the identified “anomalies” was clearly linked to stress-induced reductions in photosynthesis. Furthermore, the percentage of these “anomalies” showed a meaningful correlation with both the progression and severity of stress. The results highlight the still largely unexploited potential of Machine Learning in OJIP analysis. Full article

LoRa networks, widely adopted for low-power, long-range communication in IoT applications, face critical security concerns as radio-frequency transmissions are increasingly vulnerable to tampering. This paper addresses the dual challenges of privacy-preserving detection of tampered transmissions and the identification of unknown attacks in LoRa-based IoT networks. Leveraging Federated Learning (FL), our approach enables the detection of tampered RF transmissions while safeguarding sensitive IoT data, as FL allows model training on distributed devices without sharing raw data. We evaluated the performance of multiple FL-enabled anomaly-detection algorithms, including Convolutional Autoencoder Federated Learning (CAE-FL), Isolation Forest Federated Learning (IF-FL), One-Class Support Vector Machine Federated Learning (OCSVM-FL), Local Outlier Factor Federated Learning (LOF-FL), and K-Means Federated Learning (K-Means-FL). Using metrics such as accuracy, precision, recall, and F1-score, CAE-FL emerged as the top performer, achieving 97.27% accuracy and a balanced precision, recall, and F1-score of 0.97, with IF-FL close behind at 96.84% accuracy. Competitive performance from OCSVM-FL and LOF-FL, along with the comparable results of K-Means-FL, highlighted the robustness of clustering-based detection methods in this context. Visual analyses using confusion matrices and ROC curves provided further insights into each model’s effectiveness in detecting tampered signals. This research underscores the capability of federated learning to enhance privacy and security in anomaly detection for LoRa networks, even against unknown attacks, marking a significant advancement in securing IoT communications in sensitive applications. Full article

A microreactor is a chemical reaction device that mixes liquids in a very narrow channel and continuously generates reactions. They are attracting attention as next-generation chemical reaction devices because of their ability to achieve small-scale and highly efficient reactions compared to the conventional badge method. However, the challenge is to design a control system that is tolerant of faults in some of the enormous number of sensors in order to achieve parallel production by numbering up. In a previous study, a simultaneous control system for two different temperatures was proposed in an experimental system that imitated the microreactor cooled by Peltier devices. In addition, a fault-tolerant control system for one area has also been proposed. However, the fault-tolerant control system could not be applied to the control system of two temperatures in the previous study. In this paper, we extend it to a two-input, two-output fault-tolerant control system. We also use a fault detection system that combines ChangeFinder, a time-series data analysis method, and One-Class SVM, an unsupervised learning method. Finally, the effectiveness of the proposed method is confirmed by experiments. Full article

In the realm of smart grids, machine learning (ML) detectors—both binary (or supervised) and anomaly (or unsupervised)—have proven effective in detecting electricity theft (ET). However, binary detectors are designed for specific attacks, making their performance unpredictable against new attacks. Anomaly detectors, conversely, are trained on benign data and identify deviations from benign patterns as anomalies, but their performance is highly sensitive to the selected threshold values. Additionally, ML detectors are vulnerable to evasion attacks, where attackers make minimal changes to malicious samples to evade detection. To address these limitations, we introduce a hybrid anomaly detector that combines a Deep Auto-Encoder (DAE) with a One-Class Support Vector Machine (OCSVM). This detector not only enhances classification performance but also mitigates the threshold sensitivity of the DAE. Furthermore, we evaluate the vulnerability of this detector to benchmark evasion attacks. Lastly, we propose an accurate and robust cluster-based DAE+OCSVM ET anomaly detector, trained using Explainable Artificial Intelligence (XAI) explanations generated by the Shapley Additive Explanations (SHAP) method on consumption readings. Our experimental results demonstrate that the proposed XAI-based detector achieves superior classification performance and exhibits enhanced robustness against various evasion attacks, including gradient-based and optimization-based methods, under a black-box threat model. Full article

Imbalanced data situations exist in most fields of endeavor. The problem has been identified as a major bottleneck in machine learning/data mining and is becoming a serious issue of concern in food processing applications. Inappropriate analysis of agricultural and food processing data was identified as limiting the robustness of predictive models built from agri-food applications. As a result of rare cases occurring infrequently, classification rules that detect small groups are scarce, so samples belonging to small classes are largely misclassified. Most existing machine learning algorithms including the K-means, decision trees, and support vector machines (SVMs) are not optimal in handling imbalanced data. Consequently, models developed from the analysis of such data are very prone to rejection and non-adoptability in real industrial and commercial settings. This paper showcases the reality of the imbalanced data problem in agri-food applications and therefore proposes some state-of-the-art artificial intelligence algorithm approaches for handling the problem using methods including data resampling, one-class learning, ensemble methods, feature selection, and deep learning techniques. This paper further evaluates existing and newer metrics that are well suited for handling imbalanced data. Rightly analyzing imbalanced data from food processing application research works will improve the accuracy of results and model developments. This will consequently enhance the acceptability and adoptability of innovations/inventions. Full article

The advanced metering infrastructure (AMI) of the smart grid plays a critical role in energy management and billing by enabling the periodic transmission of consumers’ power consumption readings. To optimize data collection efficiency, AMI employs a “change and transmit” (CAT) approach. This approach ensures that readings are only transmitted when there is enough change in consumption, thereby reducing data traffic. Despite the benefits of this approach, it faces security challenges where malicious consumers can manipulate their readings to launch cyberattacks for electricity theft, allowing them to illegally reduce their bills. While this challenge has been addressed for supervised learning CAT settings, it remains insufficiently addressed in unsupervised learning settings. Moreover, due to the distortion introduced in the power consumption readings due to using the CAT approach, the accurate prediction of future consumption for energy management is a challenge. In this paper, we propose a two-stage approach to predict future readings and detect electricity theft in the smart grid while optimizing data collection using the CAT approach. For the first stage, we developed a predictor that is trained exclusively on benign CAT power consumption readings, and the output of the predictor is the actual readings. To enhance the prediction accuracy, we propose a cluster-based predictor that groups consumers into clusters with similar consumption patterns, and a dedicated predictor is trained for each cluster. For the second stage, we trained an autoencoder and a one-class support vector machine (SVM) on the benign reconstruction errors of the predictor to classify instances of electricity theft. We conducted comprehensive experiments to assess the effectiveness of our proposed approach. The experimental results indicate that the prediction error is very small and the accuracy of detection of the electricity theft attacks is high. Full article

Road surface quality is essential for driver comfort and safety, making it crucial to monitor pavement conditions and detect defects in real time. However, the diversity of defects and the complexity of ambient conditions make it challenging to develop an effective and robust classification and detection algorithm. In this study, we adopted a semi-supervised learning approach to train ResNet-18 for image feature retrieval and then classification and detection of pavement defects. The resulting feature embedding vectors from image patches were retrieved, concatenated, and randomly sampled to model a multivariate normal distribution based on the only one-class training pavement image dataset. The calibration pavement image dataset was used to determine the defect score threshold based on the receiver operating characteristic curve, with the Mahalanobis distance employed as a metric to evaluate differences between normal and defect pavement images. Finally, a heatmap derived from the defect score map for the testing dataset was overlaid on the original pavement images to provide insight into the network’s decisions and guide measures to improve its performance. The results demonstrate that the model’s classification accuracy improved from 0.868 to 0.887 using the expanded and augmented pavement image data based on the analysis of heatmaps. Full article