Machine learning anomaly detection in large systems

Murphree, Jerry

doi:10.1109/autest.2016.7589589

Cited by 27 publications

(17 citation statements)

References 5 publications

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“…In the studies which use the autoencoder approach on the other hand, the number of different attributes used was 21 (Gomes et al, 2017), 18 (Domingos et al, 2016;Paula et al, 2016) and 10 (Schreyer et al, 2017). A reason for this could be that autoencoders can handle thousands of different dimensions (Murphree, 2016).…”

Section: Methodsmentioning

confidence: 99%

Unsupervised anomaly detection for internal auditing: Literature review and research agenda

Nonnenmacher¹,

Gómez²

2021

IJDAR

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Auditing has to adapt to the growing amounts of data caused by digital transformation. One approach to address this and to test the full audit data population is to apply rules to the data. A disadvantage of this is that rules most likely only find errors, mistakes or deviations which were already anticipated by the auditor. Unsupervised anomaly detection can go beyond those capabilities and detect novel process deviations or new fraud attempts. We conducted a systematic review of existing studies which apply unsupervised anomaly detection in an auditing context. The results reveal that most of the studies develop an approach for only one specific dataset and do not address the integration into the audit process or how the results should be best presented to the auditor. We therefore develop a research agenda addressing both the generalizability of unsupervised anomaly detection in auditing and the preparation of results for auditors.

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Section: Methodsmentioning

confidence: 99%

Unsupervised anomaly detection for internal auditing: Literature review and research agenda

Nonnenmacher¹,

Gómez²

2021

IJDAR

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“…An illustration of an autoencoder architecture is presented below in Figure 1 . Autoencoders have been widely used for dimensionality reduction applications [ 41 ], signal reconstruction applications, and anomaly detection applications [ 37 , 42 , 43 ].…”

Section: Research Backgroundmentioning

confidence: 99%

Predictive Maintenance in Building Facilities: A Machine Learning-Based Approach

Bouabdallaoui

Lafhaj

Yim

et al. 2021

Sensors

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The operation and maintenance of buildings has seen several advances in recent years. Multiple information and communication technology (ICT) solutions have been introduced to better manage building maintenance. However, maintenance practices in buildings remain less efficient and lead to significant energy waste. In this paper, a predictive maintenance framework based on machine learning techniques is proposed. This framework aims to provide guidelines to implement predictive maintenance for building installations. The framework is organised into five steps: data collection, data processing, model development, fault notification and model improvement. A sport facility was selected as a case study in this work to demonstrate the framework. Data were collected from different heating ventilation and air conditioning (HVAC) installations using Internet of Things (IoT) devices and a building automation system (BAS). Then, a deep learning model was used to predict failures. The case study showed the potential of this framework to predict failures. However, multiple obstacles and barriers were observed related to data availability and feedback collection. The overall results of this paper can help to provide guidelines for scientists and practitioners to implement predictive maintenance approaches in buildings.

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“…Authors in [18] have proposed a data-driven risk management framework based on time series data analyses, while the frameworks presented by [3] and [19] respectively address energy saving and optimization and product life-cycle management and maintenance. With the goal of detecting the health of a system, [20] has applied anomaly detection algorithms to detect failure or a pending failure from the system measurements. A particular attention has also been paid to the application of Deep Learning models in prognostics (e.g., [19], [21]).…”

Section: Literature Reviewmentioning

confidence: 99%

REDTag: A Predictive Maintenance Framework for Parcel Delivery Services

et al. 2020

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The overwhelming increase of parcel transports has prompted the need for effective and scalable intelligent logistics systems. In parallel, with the advent of Industry 4.0, a tight integration of Internet of Things technologies and Big Data analytics solution has become necessary to effectively manage industrial processes and to early predict product faults or service disruptions. In the context of good transports, the development of smart monitoring tools is particularly useful for couriers to ensure effective and efficient parcel deliveries. However, the existing predictive maintenance frameworks are not tailored to parcel delivery services. We present REDTag Service, an integrated framework to track and monitor the shipped packages. It relies on a network of IoT-enabled devices, called REDTags, allowing courier employees to easily collect the status of the package at each delivery step. The framework provides back-end functionalities for smart data transmission, management, storage, and analytics. A machine-learning process is included to promptly analyze the features describing event-related data to predict potential breaks of the goods in the packages. The framework provides also a dynamic view on the integrated data tailored to the different stakeholders, as well as on the prediction outcomes, enabling immediate feedback and model improvements. We analyze a realworld dataset including event-related data about parcel transports. To validate the hypothesis that the acquired data contains information relevant to predict the package status (i.e., broken or safe), we empirically analyze the performance of different, scalable classifiers. The experimental results confirm, in good approximation, the predictive power of the models extracted from the event-related features. To the best of the authors' knowledge, this work is the first attempt to address predictive maintenance in smart good transport logistics to predict package breaks from real-world data. INDEX TERMS Big data analytics, Industry 4.0, intelligent transports and logistics, Internet of Things, machine learning, predictive maintenance.

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Machine learning anomaly detection in large systems

Cited by 27 publications

References 5 publications

Unsupervised anomaly detection for internal auditing: Literature review and research agenda

Unsupervised anomaly detection for internal auditing: Literature review and research agenda

Predictive Maintenance in Building Facilities: A Machine Learning-Based Approach

REDTag: A Predictive Maintenance Framework for Parcel Delivery Services

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