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Surveillance for genetic variation of microbial pathogens, both within and among species, plays an important role in informing research, diagnostic, prevention, and treatment activities for disease control. However, large-scale systematic screening for novel genotypes remains challenging in part due to technological limitations. Towards addressing this challenge, we present an advancement in universal microbial high resolution melting (HRM) analysis that is capable of accomplishing both known genotype identification and novel genotype detection. Specifically, this novel surveillance functionality is achieved through time-series modeling of sequence-defined HRM curves, which is uniquely enabled by the large-scale melt curve datasets generated using our high-throughput digital HRM platform. Taking the detection of bacterial genotypes as a model application, we demonstrate that our algorithms accomplish an overall classification accuracy over 99.7% and perform novelty detection with a sensitivity of 0.96, specificity of 0.96 and Youden index of 0.92. Since HRM-based DNA profiling is an inexpensive and rapid technique, our results add support for the feasibility of its use in surveillance applications.

Background: Graphical representations are useful to model complex data in general and biological interactions in particular. Our main motivation is the comparison of metabolic networks in the wider context of developing noninvasive accurate diagnostic tools. However, comparison and classification of graphs is still extremely challenging, although a number of highly efficient methods such as graph neural networks were developed in the recent decade. Important aspects are still lacking in graph classification: interpretability and guarantees on classification quality, i.e., control of the risk level or false discovery rate control. Results: In our contribution, we introduce a statistically sound approach to control the false discovery rate in a classification task for graphs in a semi-supervised setting. Our procedure identifies novelties in a dataset, where a graph is considered to be a novelty when its topology is significantly different from those in the reference class. It is noteworthy that the procedure is a conformal prediction approach, which does not make any distributional assumptions on the data and that can be seen as a wrapper around traditional machine learning models, so that it takes full advantage of existing methods. The performance of the proposed method is assessed on several standard benchmarks. It is also adapted and applied to the difficult task of classifying metabolic networks, where each graph is a representation of all metabolic reactions of a bacterium and to real task from a cancer data repository. Conclusions: Our approach efficiently controls — in highly complex data — the false discovery rate, while maximizing the true discovery rate to get the most reasonable predictive performance. This contribution is focused on confident classification of complex data, what can be further used to explore complex human pathologies and their mechanisms. [ABSTRACT FROM AUTHOR]

Based on the fast kurtogram, many previous studies have focused on frequency band selection (FBS) affected by interference due to impulsive noise and varying speed conditions. Recently, machine learning algorithms have been considered effective for bearing diagnosis. However, most of these methods are supervised learning and thus suffer from data imbalance in the practical applications. This study proposes a semi-supervised learning indicator called outlier rate diagram (ORgram) based on the signal changes before and after bearing damage. ORgram is suitable for impulsive noise and varying speed conditions. First, the healthy bearing data is segmented by rotational speed and modeled with one-class support vector machine (SVM) for each frequency band. As bearing defects sprout, the defect response will produce the highest outlier rate in the informative frequency band (IFB). After selecting the IFB, techniques such as envelope analysis and order tracking are used to identify damaged bearing components for an alarm to reduce the losses caused by unplanned downtime. [ABSTRACT FROM AUTHOR]

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Abstract Background Graphical representations are useful to model complex data in general and biological interactions in particular. Our main motivation is the comparison of metabolic networks in the wider context of developing noninvasive accurate diagnostic tools. However, comparison and classification of graphs is still extremely challenging, although a number of highly efficient methods such as graph neural networks were developed in the recent decade. Important aspects are still lacking in graph classification: interpretability and guarantees on classification quality, i.e., control of the risk level or false discovery rate control. Results In our contribution, we introduce a statistically sound approach to control the false discovery rate in a classification task for graphs in a semi-supervised setting. Our procedure identifies novelties in a dataset, where a graph is considered to be a novelty when its topology is significantly different from those in the reference class. It is noteworthy that the procedure is a conformal prediction approach, which does not make any distributional assumptions on the data and that can be seen as a wrapper around traditional machine learning models, so that it takes full advantage of existing methods. The performance of the proposed method is assessed on several standard benchmarks. It is also adapted and applied to the difficult task of classifying metabolic networks, where each graph is a representation of all metabolic reactions of a bacterium and to real task from a cancer data repository. Conclusions Our approach efficiently controls — in highly complex data — the false discovery rate, while maximizing the true discovery rate to get the most reasonable predictive performance. This contribution is focused on confident classification of complex data, what can be further used to explore complex human pathologies and their mechanisms.

Detecting texts that contain semantic-level new information is not straightforward. The problem becomes more challenging for research articles. Over the years, many datasets and techniques have been developed to attempt automatic novelty detection. However, the majority of the existing textual novelty detection investigations are targeted toward general domains like newswire. A comprehensive dataset for scientific novelty detection is not available in the literature. In this paper, we present a new triplet-based corpus (SciND) for scientific novelty detection from research articles via knowledge graphs. The proposed dataset consists of three types of triples (i) triplet for the knowledge graph, (ii) novel triplets, and (iii) non-novel triplets. We build a scientific knowledge graph for research articles using triplets across several natural language processing (NLP) domains and extract novel triplets from the paper published in the year 2021. For the non-novel articles, we use blog post summaries of the research articles. Our knowledge graph is domain-specific. We build the knowledge graph for seven NLP domains. We further use a feature-based novelty detection scheme from the research articles as a baseline. Moreover, we show the applicability of our proposed dataset using our baseline novelty detection algorithm. Our algorithm yields a baseline F1 score of 72%. We show analysis and discuss the future scope using our proposed dataset. To the best of our knowledge, this is the very first dataset for scientific novelty detection via a knowledge graph. We make our codes and dataset publicly available at https://github.com/92Komal/Scientific_Novelty_Detection. [ABSTRACT FROM AUTHOR]

Historical fine-scale information of archaeological landscapes is crucial in archaeological investigations. However, documenting such information using satellite sensor data prior to 2000 remains a daunting challenge. Images from the declassified archives of KH-9 HEXAGON (KH-9) cameras, including the panoramic camera system (PCS) and mapping camera system (MCS), offer fine-scale information about archaeological sites. However, noise, contrast distortion and the availability of only a single panchromatic band can limit their potential, particularly for identifying features in subtropical climates within heterogeneous landscape types. This paper focuses on developing a novel multifaceted analytical framework with two components: image pre-processing and feature identification. The image pre-processing component is divided into two steps. First, a trained stationary wavelet transform (SWT) based on the normalized sill (NS) is developed to not only de-noises the image, but also preserve its original image characteristics. Then, the contrast of the de-noised images is optimized by the multi-resolution Top-hat (MTH) using multi-scale information. In the feature identification component, the MCS image is analysed using spatial colour composite write function memory (SCCWFM) and spatial novelty detection (SND). An ultra-fine spatial three-dimensional colour composite (UFSTCC) image and ultra-fine spatial digital surface model (UFSDSM) are produced to aid interpretation of the KH-9 PCS images. The proposed processing pipelines were tested on KH-9 MCS and PCS images of the World Heritage site at Liangzhu Ancient City (LAC) in China, which is characterized by a subtropical climate and a heterogeneous landscape types. The proposed pre-processing pipeline improved considerably the appearance of these images across the LAC landscape while maintaining the original image information. The developed digital analytical approaches for KH-9 PCS and MCS images facilitated straightforward identification of archaeological features in the LAC. The proposed framework has the potential to increase exploitation of the available KH-9 images in archaeological applications. [ABSTRACT FROM AUTHOR]

As the number of European Union (EU) visitors grows, implementing novel border control solutions, such as mobile devices for passenger identification for land and sea border control, becomes paramount to ensure the convenience and safety of passengers and officers. However, these devices, handling sensitive personal data, become attractive targets for malicious actors seeking to misuse or steal such data. Therefore, to increase the level of security of such devices without interrupting border control activities, robust user authentication mechanisms are essential. Toward this direction, we propose a risk-based adaptive user authentication mechanism for mobile passenger identification devices for land and sea border control, aiming to enhance device security without hindering usability. In this work, we present a comprehensive assessment of novelty and outlier detection algorithms and discern OneClassSVM, Local Outlier Factor (LOF), and Bayesian_GaussianMixtureModel (B_GMM) novelty detection algorithms as the most effective ones for risk estimation in the proposed mechanism. Furthermore, in this work, we develop the proposed risk-based adaptive user authentication mechanism as an application on a Raspberry Pi 4 Model B device (i.e., playing the role of the mobile device for passenger identification), where we evaluate the detection performance of the three best performing novelty detection algorithms (i.e., OneClassSVM, LOF, and B_GMM), with B_GMM surpassing the others in performance when deployed on the Raspberry Pi 4 device. Finally, we evaluate the risk estimation overhead of the proposed mechanism when the best performing B_GMM novelty detection algorithm is used for risk estimation, indicating efficient operation with minimal additional latency. [ABSTRACT FROM AUTHOR]

In this work, we propose a one-class self-supervised method for anomaly segmentation in images that benefits from both a modern machine learning approach and a more classic statistical detection theory. The method consists of four phases. First, features are extracted using a multi-scale image transformer architecture. Then, these features are fed into a U-shaped normalizing flow (NF) that lays the theoretical foundations for the subsequent phases. The third phase computes a pixel-level anomaly map from the NF embedding, and the last phase performs a segmentation based on the a contrario framework. This multiple hypothesis testing strategy permits the derivation of robust unsupervised detection thresholds, which are crucial in real-world applications where an operational point is needed. The segmentation results are evaluated using the mean intersection over union metric, and for assessing the generated anomaly maps we report the area under the receiver operating characteristic curve (AUROC), as well as the area under the per-region-overlap curve (AUPRO). Extensive experimentation in various datasets shows that the proposed approach produces state-of-the-art results for all metrics and all datasets, ranking first in most MVTec-AD categories, with a mean pixel-level AUROC of 98.74%. Code and trained models are available at https://github.com/mtailanian/uflow. [ABSTRACT FROM AUTHOR]

Processing large and heterogeneous numbers of ideas submitted to crowdsourcing contests is a regular challenge for idea evaluators. The aim of this study is to investigate a potential use case for AI-based innovation management and to extend the knowledge of using automated novelty detection in idea evaluation processes. AI-based language models can automatically allocate short texts according to their semantic similarity in an embedded space. We represent the semantic content of crowdsourced ideas with the three contemporary text embeddings – Doc2Vec, SBERT, and GPT-3-based Ada Similarity – and compute their semantic distance to different reference sets using different novelty detection algorithms. We then compare the algorithm-generated scores with human novelty assessments to validate them. While selected novelty scores based on text embeddings correlate with humans, our results show that scores based on SBERT embeddings best match human novelty assessments. We also find that AI-based novelty detection approaches perform better for ideas below the median word count and when compared to a set of existing solutions, suggesting that the chosen language model is not the only factor influencing the applicability of the proposed approach. Furthermore, the study highlights important features and limitations of automatically generated novelty scores that need to be considered when complementing evaluators searching for new ideas in crowdsourcing contests and beyond. [ABSTRACT FROM AUTHOR]

To solve the problem that novelty detection is difficult to identify the time and location of structural damage at the same time, convolutional neural network was introduced into novelty detection. Firstly, the wavelet packet technology was used to process the structural response to obtain the wavelet packet energy, and the energy ratio of the corresponding frequency bands of adjacent measurement points was applied as the feature vector of the novelty detection model. Then, taking the eigenvector of the healthy structure as the training data, the novelty detection model based on convolutional neural network under the health mode was established. Next, the real-time output feature vectors of the structure was input into the novelty detection model, the output with was compared the output of the health state, and the Euclidean distance between the output and input was used as the novelty index. Finally, the time and location of structural damage based on changes were identified in novelty index. The effectiveness of the method herein were verified through numerical simulation and laboratory experiments. [ABSTRACT FROM AUTHOR]

Abstract Surveillance for genetic variation of microbial pathogens, both within and among species, plays an important role in informing research, diagnostic, prevention, and treatment activities for disease control. However, large-scale systematic screening for novel genotypes remains challenging in part due to technological limitations. Towards addressing this challenge, we present an advancement in universal microbial high resolution melting (HRM) analysis that is capable of accomplishing both known genotype identification and novel genotype detection. Specifically, this novel surveillance functionality is achieved through time-series modeling of sequence-defined HRM curves, which is uniquely enabled by the large-scale melt curve datasets generated using our high-throughput digital HRM platform. Taking the detection of bacterial genotypes as a model application, we demonstrate that our algorithms accomplish an overall classification accuracy over 99.7% and perform novelty detection with a sensitivity of 0.96, specificity of 0.96 and Youden index of 0.92. Since HRM-based DNA profiling is an inexpensive and rapid technique, our results add support for the feasibility of its use in surveillance applications.

Introduction: Novelty detection (ND, also known as one-class classification) is a machine learning technique used to identify patterns that are typical of the majority class and can discriminate deviations as novelties. In the context of Alzheimer's disease (AD), ND could be employed to detect abnormal or atypical behavior thatmay indicate early signs of cognitive decline or the presence of the disease. To date, few research studies have used ND to discriminate the risk of developing AD and mild cognitive impairment (MCI) from healthy controls (HC). Methods: In this work, two distinct cohorts with highly heterogeneous data, derived from the Australian Imaging Biomarkers and Lifestyle (AIBL) Flagship Study of Ageing project and the Fujian Medical University Union Hospital (FMUUH) China, were employed. An innovative framework with built-in easily interpretable ND models constructed solely on HC data was introduced along with proposing a strategy of distance to boundary (DtB) to detect MCI and AD. Subsequently, a web-based graphical user interface (GUI) that incorporates the proposed framework was developed for non-technical stakeholders. Results: Our experimental results indicate that the best overall performance of detecting AD individuals in AIBL and FMUUH datasets was obtained by using the Mixture of Gaussian-based ND algorithmapplied to singlemodality, with an AUC of 0.8757 and 0.9443, a sensitivity of 96.79% and 89.09%, and a specificity of 89.63% and 90.92%, respectively. Discussion: The GUI offers an interactive platform to aid stakeholders in making diagnoses of MCI and AD, enabling streamlined decision-making processes. More importantly, the proposed DtB strategy could visually and quantitatively identify individuals at risk of developing AD. [ABSTRACT FROM AUTHOR]

In this article, we propose a dual-stage classification framework designed for identifying rare or unseen patterns in the hard disk drive (HDD) industrial test process. The proposed framework integrates novelty detection and supervised learning methodologies to effectively address the challenges associated with imbalanced datasets and the detection of infrequent or unseen patterns within continuously changing environments. By employing novelty detection as the first-stage classifier followed by supervised learning as the second-stage classifier, the proposed method demonstrates an increased capacity to adapt to fluctuating environments, consequently enhancing the overall accuracy of process classification in practical manufacturing settings. To strengthen the robustness of novelty detection methods, an ensemble model technique is employed. Notably, the accuracy of the novelty detection methods in the first stage can be further enhanced with the incorporation of supervised learning techniques, particularly when a sufficiently large number of labeled samples are amassed. The proposed method consistently maintains accuracy, even in the face of changing environments, as it demonstrates the ability to adapt to data drift without necessitating the acquisition of new labeled data in the initial stage. This adaptability makes it particularly well suited for managing imbalanced datasets, rendering it highly practical for industrial applications. In a comprehensive case study conducted within the HDD industry, the framework exhibits immediate adaptability to rapidly changing environments while preserving high accuracy. This highlights the practical effectiveness of the proposed dual-stage classification framework in addressing the unique challenges posed by industrial scenarios. [ABSTRACT FROM AUTHOR]

Novelty detection and concept drift detection are essential for the plethora of machine learning applications. The statistical properties of application generated data change over time in the streaming environment, known as concept drift. These changes develop a profound influence on the learning model's performance. Along with concept drift, the new class emergence (i.e., novel class/novelty detection) is also challenging in the non-stationary distribution of data. Novel class detection finds whether the identifying data points of a data stream are unknown or unusual. The paper presents a survey focusing on the challenges encountered while dealing with real-time data. In addition to this, the chronological discussion on the various existing novelty detectors with their advantages, limitations, critical points, the different research prospect, and future directions are also incorporated in the paper. [ABSTRACT FROM AUTHOR]

In the dynamic and competitive telecommunications industry, effective risk management is critical. Traditional methods are often manual and labor-intensive, prone to inaccuracies and inefficiencies. This paper addresses these challenges by integrating advanced machine learning algorithms to automate and improve the risk management process at a large telecommunications company, hereafter referred to as TP. The study applies state-of-the-art transformer-based machine learning models - BERT, RoBERTa, DeBERTa, and ERNIE 2.0 - alongside a classical support vector machine (SVM) for comparative analysis. The approach leverages the Cross-Industry Standard Process for Data Mining (CRISP-DM) model, emphasizes text classification algorithms to categorize risk events, and introduces a one-class SVM for novelty detection to identify unprecedented risk events. The results show that transformer-based models achieve superior prediction accuracy over traditional SVMs, with the best-performing transformer model (DeBERTa) achieving an F1 score of 92.5%, significantly improving the risk classification accuracy. In addition, the novelty detection mechanism successfully identifies novel risk events with an accuracy of 77.2% and an AUROC of 79.2%. This integration streamlines risk management processes and provides operational benefits to telecommunications companies. By providing insight into these technologies’ economic and operational benefits, this study contributes to the practical application of AI in telecommunications, particularly in risk event assessment and natural language processing.

Gas turbines play a key role in generating power. It is really important that they work efficiently, safely, and reliably. However, their performance can be adversely affected by factors such as component wear, vibrations, and temperature fluctuations, often leading to abnormal patterns indicative of potential failures. As a result, anomaly detection has become an area of active research. Matrix Profile (MP) methods have emerged as a promising solution for identifying significant deviations in time series data from normal operational patterns. While most existing MP methods focus on vibration analysis of gas turbines, this paper introduces a novel approach using the outlet power signal. This modified approach, termed Cluster-based Matrix Profile (CMP) analysis, facilitates the identification of abnormal patterns and subsequent anomaly detection within the gas turbine engine system. Significantly, CMP analysis not only accelerates processing speed, but also provides user-friendly support information for operators. The experimental results on real-world gas turbines demonstrate the effectiveness of our approach in the early detection of anomalies and potential system failures. [ABSTRACT FROM AUTHOR]

As the number of European Union (EU) visitors grows, implementing novel border control solutions, such as mobile devices for passenger identification for land and sea border control, becomes paramount to ensure the convenience and safety of passengers and officers. However, these devices, handling sensitive personal data, become attractive targets for malicious actors seeking to misuse or steal such data. Therefore, to increase the level of security of such devices without interrupting border control activities, robust user authentication mechanisms are essential. Toward this direction, we propose a risk-based adaptive user authentication mechanism for mobile passenger identification devices for land and sea border control, aiming to enhance device security without hindering usability. In this work, we present a comprehensive assessment of novelty and outlier detection algorithms and discern OneClassSVM, Local Outlier Factor (LOF), and Bayesian_GaussianMixtureModel (B_GMM) novelty detection algorithms as the most effective ones for risk estimation in the proposed mechanism. Furthermore, in this work, we develop the proposed risk-based adaptive user authentication mechanism as an application on a Raspberry Pi 4 Model B device (i.e., playing the role of the mobile device for passenger identification), where we evaluate the detection performance of the three best performing novelty detection algorithms (i.e., OneClassSVM, LOF, and B_GMM), with B_GMM surpassing the others in performance when deployed on the Raspberry Pi 4 device. Finally, we evaluate the risk estimation overhead of the proposed mechanism when the best performing B_GMM novelty detection algorithm is used for risk estimation, indicating efficient operation with minimal additional latency.

The scores of distance-based outlier detection methods are difficult to interpret, and it is challenging to determine a suitable cut-off threshold between normal and outlier data points without additional context. We describe a generic transformation of distance-based outlier scores into interpretable, probabilistic estimates. The transformation is ranking-stable and increases the contrast between normal and outlier data points. Determining distance relationships between data points is necessary to identify the nearest-neighbor relationships in the data, yet most of the computed distances are typically discarded. We show that the distances to other data points can be used to model distance probability distributions and, subsequently, use the distributions to turn distance-based outlier scores into outlier probabilities. Over a variety of tabular and image benchmark datasets, we show that the probabilistic transformation does not impact outlier ranking (ROC AUC) or detection performance (AP, F1), and increases the contrast between normal and outlier score distributions (statistical distance). The experimental findings indicate that it is possible to transform distance-based outlier scores into interpretable probabilities with increased contrast between normal and outlier samples. Our work generalizes to a wide range of distance-based outlier detection methods, and, because existing distance computations are used, it adds no significant computational overhead.

The building sector significantly contributes to climate change. To improve its carbon footprint, applications like model predictive control and predictive maintenance rely on system models. However, the high modeling effort hinders practical application. Machine learning models can significantly reduce this modeling effort. To ensure a machine learning model’s reliability in all operating states, it is essential to know its validity domain. Operating states outside the validity domain might lead to extrapolation, resulting in unpredictable behavior. This paper addresses the challenge of identifying extrapolation in data-driven building energy system models and aims to raise knowledge about it. For that, a novel approach is proposed that calibrates novelty detection algorithms towards the machine learning model. Suitable novelty detection algorithms are identified through a literature review and a benchmark test with 15 candidates. A subset of five algorithms is then evaluated on building energy systems. First, on two-dimensional data, displaying the results with a novel visualization scheme. Then on more complex multi-dimensional use cases. The methodology performs well, and the validity domain could be approximated. The visualization allows for a profound analysis and an improved understanding of the fundamental effects behind a machine learning model’s validity domain and the extrapolation regimes.

Introduction: The perception of phonemes is guided by both low-level acoustic cues and high-level linguistic context. However, differentiating between these two types of processing can be challenging. In this study, we explore the utility of pupillometry as a tool to investigate both low- and high-level processing of phonological stimuli, with a particular focus on its ability to capture novelty detection and cognitive processing during speech perception. Methods: Pupillometric traces were recorded from a sample of 22 Danishspeaking adults, with self-reported normal hearing, while performing two phonological-contrast perception tasks: a nonword discrimination task, which included minimal-pair combinations specific to the Danish language, and a nonword detection task involving the detection of phonologically modified words within sentences. The study explored the perception of contrasts in both unprocessed speech and degraded speech input, processed with a vocoder. Results: No difference in peak pupil dilation was observed when the contrast occurred between two isolated nonwords in the nonword discrimination task. For unprocessed speech, higher peak pupil dilations were measured when phonologically modified words were detected within a sentence compared to sentences without the nonwords. For vocoded speech, higher peak pupil dilation was observed for sentence stimuli, but not for the isolated nonwords, although performance decreased similarly for both tasks. Conclusion: Our findings demonstrate the complexity of pupil dynamics in the presence of acoustic and phonological manipulation. Pupil responses seemed to reflect higher-level cognitive and lexical processing related to phonological perception rather than low-level perception of acoustic cues. However, the incorporation of multiple talkers in the stimuli, coupled with the relatively low task complexity, may have affected the pupil dilation. [ABSTRACT FROM AUTHOR]

In mouse primary visual cortex (V1), familiar stimuli evoke significantly altered responses when compared with novel stimuli. This stimulus-selective response plasticity (SRP) was described originally as an increase in the magnitude of visual evoked potentials (VEPs) elicited in layer 4 (L4) by familiar phase-reversing grating stimuli. SRP is dependent on NMDA receptors (NMDARs) and has been hypothesized to reflect potentiation of thalamocortical (TC) synapses in L4. However, recent evidence indicates that the synaptic modifications that manifest as SRP do not occur on L4 principal cells. To shed light on where and how SRP is induced and expressed in male and female mice, the present study had three related aims: (1) to confirm that NMDAR are required specifically in glutamatergic principal neurons of V1, (2) to investigate the consequences of deleting NMDAR specifically in L6, and (3) to use translaminar electrophysiological recordings to characterize SRP expression in different layers of V1. We find that knock-out (KO) of NMDAR in L6 principal neurons disrupts SRP. Current-source density (CSD) analysis of the VEP depth profile shows augmentation of short latency current sinks in layers 3, 4, and 6 in response to phase reversals of familiar stimuli. Multiunit recordings demonstrate that increased peak firing occurs in response to phase reversals of familiar stimuli across all layers, but that activity between phase reversals is suppressed. Together, these data reveal important aspects of the underlying phenomenology of SRP and generate new hypotheses for the expression of experience-dependent plasticity in V1. [ABSTRACT FROM AUTHOR]

Digitisation offers manufacturing companies new opportunities to improve their operations and competitiveness in the market by unleashing potentialities related to real-time monitoring and control of operating machines. Through condition-based and predictive maintenance, the knowledge about the health state and probability of failure of the machines is improved for better decision-making. Amongst them, CNC machine tools do represent a complex case from a maintenance viewpoint as their operations are ever-changing and their high reliability brings to a lack, or limited set, of run-to-failure data. To address the problem, the research work proposes an operations-aware novelty detection framework for CNC machine tools based on already-in-place controllers. The framework is based on statistical modelling of the behaviour of the machine tools, namely through gradient boosting regression and Gaussian mixture models, to identify the health state considering varying operations through time. The proposed solution is verified on sixteen multi-axis CNC machine tools in a large manufacturing company. The results show that the proposed solution can effectively support maintenance decisions by informing on the health states while discerning between varying operations and abnormal/faulty states of interest. This solution represents a brick in a cloud-edge-based industrial information system stack that can be further developed for shop floor-integrated decision-making. [ABSTRACT FROM AUTHOR]

Aiming at the problem of current equipment fault diagnosis models based on deep learning being unable to automatically identify new class faults according to the updated fault data, in this paper we propose an incremental single-class fault diagnosis method based on an online sequential extreme learning machine (OS-ELM). In addition to detecting new types of faults, this method can perform class-incremental learning based on new-class fault data, treating the new-class faults as known faults for ongoing fault detection and diagnosis tasks. This approach first constructs a feature extraction network with a dual-encoder structure to extract data features. Subsequently, the extracted features are used to build a fault diagnosis network based on OS-ELM, where the novelty of new batches of data is determined by the update magnitude of OS-ELM. When a new-class fault is detected, a new OS-ELM representing the current new class is constructed using the new batch of data and added to the fault diagnosis network, thereby achieving incremental model updates. The proposed method is validated through experiments on the CWRU dataset and MFPT dataset. The results demonstrate that the accuracy of this method on the CWRU dataset is 99.62%, while on the MFPT dataset it reaches 98.80%. Compared to other incremental single-class models, this method exhibits excellent fault recognition and diagnosis capabilities. [ABSTRACT FROM AUTHOR]

The scores of distance-based outlier detection methods are difficult to interpret, and it is challenging to determine a suitable cut-off threshold between normal and outlier data points without additional context. We describe a generic transformation of distance-based outlier scores into interpretable, probabilistic estimates. The transformation is ranking-stable and increases the contrast between normal and outlier data points. Determining distance relationships between data points is necessary to identify the nearest-neighbor relationships in the data, yet most of the computed distances are typically discarded. We show that the distances to other data points can be used to model distance probability distributions and, subsequently, use the distributions to turn distance-based outlier scores into outlier probabilities. Over a variety of tabular and image benchmark datasets, we show that the probabilistic transformation does not impact outlier ranking (ROC AUC) or detection performance (AP, F1), and increases the contrast between normal and outlier score distributions (statistical distance). The experimental findings indicate that it is possible to transform distance-based outlier scores into interpretable probabilities with increased contrast between normal and outlier samples. Our work generalizes to a wide range of distance-based outlier detection methods, and, because existing distance computations are used, it adds no significant computational overhead. [ABSTRACT FROM AUTHOR]