For predictive maintenance, we examine one of the largest public datasets for machine failures derived along with their corresponding precursors as error rates, historical part replacements, and sensor inputs. To simplify the time and accuracy comparison between 27 different algorithms, we treat the imbalance between normal and failing states with nominal under-sampling. We identify 3 promising regression and discriminant algorithms with both higher accuracy (96%) and twenty-fold faster execution times than previous work. Because predictive maintenance success hinges on input features prior to prediction, we provide a methodology to rank-order feature importance and show that for this dataset, error counts prove more predictive than scheduled maintenance might imply solely based on more traditional factors such as machine age or last replacement times.
IBM Research Zurich（IBM苏黎世研究院）；IBM TSS（IBM技术支持服务部）
In predictive maintenance, model performance is usually assessed by means of precision, recall, and F1-score. However, employing the model with best performance, e.g. highest F1-score, does not necessarily result in minimum maintenance cost, but can instead lead to additional expenses. Thus, we propose to perform model selection based on the economic costs associated with the particular maintenance application. We show that cost-sensitive learning for predictive maintenance can result in significant cost reduction and fault tolerant policies, since it allows to incorporate various business con…
Nanyang Technological University（南洋理工大学）；Singapore University of Technology and Design（新加坡科技设计大学）
Failure of mission-critical equipment interrupts production and results in monetary loss. The risk of unplanned equipment downtime can be minimized through Predictive Maintenance of revenue generating assets to ensure optimal performance and safe operation of equipment. However, the increased sensorization of the equipment generates a data deluge, and existing machine-learning based predictive model alone becomes inadequate for timely equipment condition predictions. In this paper, a model-free Deep Reinforcement Learning algorithm is proposed for predictive equipment maintenance from an equip…
Mondragon Unibertsitatea - Goi Eskola Politeknikoa（蒙德拉贡大学-高等理工学院）
Attribute Oriented Induction (AOI) is a data mining algorithm used for extracting knowledge of relational data, taking into account expert knowledge. It is a clustering algorithm that works by transforming the values of the attributes and converting an instance into others that are more generic or ambiguous. In this way, it seeks similarities between elements to generate data groupings. AOI was initially conceived as an algorithm for knowledge discovery in databases, but over the years it has been applied to other areas such as spatial patterns, intrusion detection or strategy making. In this …
Dept. of Informatics, Aristotle University of Thessaloniki（塞萨洛尼基亚里士多德大学信息学系）
The use of machine learning rapidly increases in high-risk scenarios where decisions are required, for example in healthcare or industrial monitoring equipment. In crucial situations, a model that can offer meaningful explanations of its decision-making is essential. In industrial facilities, the equipment's well-timed maintenance is vital to ensure continuous operation to prevent money loss. Using machine learning, predictive and prescriptive maintenance attempt to anticipate and prevent eventual system failures. This paper introduces a visualisation tool incorporating interpretations to disp…
ExxonMobil Research and Engineering Company（埃克森美孚研究与工程公司）
As Artificial Intelligent (AI) technology advances and increasingly large amounts of data become readily available via various Industrial Internet of Things (IIoT) projects, we evaluate the state of the art of predictive maintenance approaches and propose our innovative framework to improve the current practice. The paper first reviews the evolution of reliability modelling technology in the past 90 years and discusses major technologies developed in industry and academia. We then introduce the next generation maintenance framework - Intelligent Maintenance, and discuss its key components. Thi…
Nanyang Business School（南洋商学院）；School of Civil and Environmental Engineering（土木与环境工程学院）；Emory University（埃默里大学）；CITIC Envirotech Ltd（中信环境技术有限公司）
This paper provides an economic perspective on the predictive maintenance of filtration units. The rise of predictive maintenance is possible due to the growing trend of industry 4.0 and the availability of inexpensive sensors. However, the adoption rate for predictive maintenance by companies remains low. The majority of companies are sticking to corrective and preventive maintenance. This is not due to a lack of information on the technical implementation of predictive maintenance, with an abundance of research papers on state-of-the-art machine learning algorithms that can be used effective…
Predictive maintenance is used in industrial applications to increase machine availability and optimize cost related to unplanned maintenance. In most cases, predictive maintenance applications use output from sensors, recording physical phenomenons such as temperature or vibration which can be directly linked to the degradation process of the machine. However, in some applications, outputs from sensors are not available, and event logs generated by the machine are used instead. We first study the approaches used in the literature to solve predictive maintenance problems and present a new publ…
Mondragon Unibertsitatea（蒙德龙大学）；Electronics and Computer Science（电子与计算机科学（此处按要求不细拆到系，保留原机构粒度））
Given the growing amount of industrial data spaces worldwide, deep learning solutions have become popular for predictive maintenance, which monitor assets to optimise maintenance tasks. Choosing the most suitable architecture for each use-case is complex given the number of examples found in literature. This work aims at facilitating this task by reviewing state-of-the-art deep learning architectures, and how they integrate with predictive maintenance stages to meet industrial companies' requirements (i.e. anomaly detection, root cause analysis, remaining useful life estimation). They are cate…
University of Würzburg（维尔茨堡大学）；University of Mannheim（曼海姆大学）；Syntax Systems GmbH；MOZYS Engineering GmbH；Chair of Information Systems II（信息系统II研究所）；Software Engineering Group（软件工程组）
Production issues at Volkswagen in 2016 lead to dramatic losses in sales of up to 400 million Euros per week. This example shows the huge financial impact of a working production facility for companies. Especially in the data-driven domains of Industry 4.0 and Industrial IoT with intelligent, connected machines, a conventional, static maintenance schedule seems to be old-fashioned. In this paper, we present a survey on the current state of the art in predictive maintenance for Industry 4.0. Based on a structured literate survey, we present a classification of predictive maintenance in the cont…
Predictive maintenance (PdM) is a concept, which is implemented to effectively manage maintenance plans of the assets by predicting their failures with data driven techniques. In these scenarios, data is collected over a certain period of time to monitor the state of equipment. The objective is to find some correlations and patterns that can help predict and ultimately prevent failures. Equipment in manufacturing industry are often utilized without a planned maintenance approach. Such practise frequently results in unexpected downtime, owing to certain unexpected failures. In scheduled mainten…
With a new era of cloud and big data, Database Management Systems (DBMSs) have become more crucial in numerous enterprise business applications in all the industries. Accordingly, the importance of their proactive and preventive maintenance has also increased. However, detecting problems by predefined rules or stochastic modeling has limitations, particularly when analyzing the data on high-dimensional Key Performance Indicators (KPIs) from a DBMS. In recent years, Deep Learning (DL) has opened new opportunities for this complex analysis. In this paper, we present two complementary DL approach…
Low-Power Vibration-Based Predictive Maintenance for Industry 4.0 using Neural Networks: A Survey
FZI Research Center for Information Technology（FZI信息技术研究中心）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Siemens（西门子）；NXP Semiconductors Germany GmbH（恩智浦半导体德国有限公司）
INESC TEC（葡萄牙国家电气与计算机工程系统研究所）；University Portucalense（波尔图卡伦斯大学）；University of Porto（波尔图大学）
The paper describes the MetroPT data set, an outcome of a eXplainable Predictive Maintenance (XPM) project with an urban metro public transportation service in Porto, Portugal. The data was collected in 2022 that aimed to evaluate machine learning methods for online anomaly detection and failure prediction. By capturing several analogic sensor signals (pressure, temperature, current consumption), digital signals (control signals, discrete signals), and GPS information (latitude, longitude, and speed), we provide a dataset that can be easily used to evaluate online machine learning methods. Thi…
Existing machine learning approaches for data-driven predictive maintenance are usually black boxes that claim high predictive power yet cannot be understood by humans. This limits the ability of humans to use these models to derive insights and understanding of the underlying failure mechanisms, and also limits the degree of confidence that can be placed in such a system to perform well on future data. We consider the task of predicting hard drive failure in a data center using recent algorithms for interpretable machine learning. We demonstrate that these methods provide meaningful insights …
Aristotle University of Thessaloniki（塞萨洛尼基亚里士多德大学）；Atlantis Engineering（亚特兰蒂斯工程公司）
This work explores two approaches to event-driven predictive maintenance in Industry 4.0 that cast the problem at hand as a classification or a regression one, respectively, using as a starting point two state-of-the-art solutions. For each of the two approaches, we examine different data preprocessing techniques, different prediction algorithms and the impact of ensemble and sampling methods. Through systematic experiments regarding the aspectsmentioned above,we aimto understand the strengths of the alternatives, and more importantly, shed light on how to navigate through the vast number of s…
In the pharmaceutical industry, the maintenance of production machines must be audited by the regulator. In this context, the problem of predictive maintenance is not when to maintain a machine, but what parts to maintain at a given point in time. The focus shifts from the entire machine to its component parts and prediction becomes a classification problem. In this paper, we focus on rolling-elements bearings and we propose a framework for predicting their degradation stages automatically. Our main contribution is a k-means bearing lifetime segmentation method based on high-frequency bearing …
University of Bologna（博洛尼亚大学）；INFN（意大利国家核物理研究所）；Federal University of Lavras（拉夫拉斯联邦大学）；INFN-CNAF（意大利国家核物理研究所国家核物理计算中心）
Log-based predictive maintenance of computing centers is a main concern regarding the worldwide computing grid that supports the CERN (European Organization for Nuclear Research) physics experiments. A log, as event-oriented adhoc information, is quite often given as unstructured big data. Log data processing is a time-consuming computational task. The goal is to grab essential information from a continuously changeable grid environment to construct a classification model. Evolving granular classifiers are suited to learn from time-varying log streams and, therefore, perform online classificat…
Cross-border security is of topmost priority for societies. Economies lose billions each year due to counterfeiters and other threats. Security checkpoints equipped with X-ray Security Systems (NIIS-Non-Intrusive Inspection Systems) like airports, ports, border control and customs authorities tackle the myriad of threats by using NIIS to inspect bags, air, land, sea and rail cargo, and vehicles. The reliance on the X-ray scanning systems necessitates their continuous 24/7 functioning being provided for. Hence the need for their working condition being closely monitored and preemptive actions b…
Research on Milling Machine Predictive Maintenance Based on Machine Learning and SHAP Analysis in Intelligent Manufacturing Environment
School of Advanced Manufacturing Engineering（先进制造工程学院）；Chongqing University of Posts and Telecommunications（重庆邮电大学）；School of Computing and Information Systems（计算与信息学院）；The University of Melbourne（墨尔本大学）；School of Science, Computing and Engineering Technologies（科学、计算与工程技术学院）；Swinburne University of Technology（斯威本科技大学）；Gezhi Future Research Institute（智未来研究院）
Predictive maintenance systems have the potential to significantly reduce costs for maintaining aircraft fleets as well as provide improved safety by detecting maintenance issues before they come severe. However, the development of such systems has been limited due to a lack of publicly labeled multivariate time series (MTS) sensor data. MTS classification has advanced greatly over the past decade, but there is a lack of sufficiently challenging benchmarks for new methods. This work introduces the NGAFID Maintenance Classification (NGAFID-MC) dataset as a novel benchmark in terms of difficulty…
Aalto University（阿尔托大学）；University of Manchester（曼彻斯特大学）
Predictive maintenance (PdM) is the task of scheduling maintenance operations based on a statistical analysis of the system's condition. We propose a human-in-the-loop PdM approach in which a machine learning system predicts future problems in sets of workstations (computers, laptops, and servers). Our system interacts with domain experts to improve predictions and elicit their knowledge. In our approach, domain experts are included in the loop not only as providers of correct labels, as in traditional active learning, but as a source of explicit decision rule feedback. The system is automated…
If machine failures can be detected preemptively, then maintenance and repairs can be performed more efficiently, reducing production costs. Many machine learning techniques for performing early failure detection using vibration data have been proposed; however, these methods are often power and data-hungry, susceptible to noise, and require large amounts of data preprocessing. Also, training is usually only performed once before inference, so they do not learn and adapt as the machine ages. Thus, we propose a method of performing online, real-time anomaly detection for predictive maintenance …
The concept of a digital twin has exploded in popularity over the past decade, yet confusion around its plurality of definitions, its novelty as a new technology, and its practical applicability still exists, all despite numerous reviews, surveys, and press releases. The history of the term digital twin is explored, as well as its initial context in the fields of product life cycle management, asset maintenance, and equipment fleet management, operations, and planning. A definition for a minimally viable framework to utilize a digital twin is also provided based on seven essential elements. A …
This paper highlights the trends in the field of predictive maintenance with the use of machine learning. With the continuous development of the Fourth Industrial Revolution, through IoT, the technologies that use artificial intelligence are evolving. As a result, industries have been using these technologies to optimize their production. Through scientific research conducted for this paper, conclusions were drawn about the trends in Predictive Maintenance applications with the use of machine learning bridging Artificial Intelligence and IoT. These trends are related to the types of industries…
In this work, a novel predictive maintenance system is presented and applied to the main components of wind turbines. The proposed model is based on machine learning and statistical process control tools applied to SCADA (Supervisory Control And Data Acquisition) data of critical components. The test campaign was divided into two stages: a first two years long offline test, and a second one year long real-time test. The offline test used historical faults from six wind farms located in Italy and Romania, corresponding to a total of 150 wind turbines and an overall installed nominal power of 28…
ICTEAM Institute, UCLouvain（鲁汶大学ICTEAM研究所）；Ion Beam Applications（离子束应用公司）
Predicting incoming failures and scheduling maintenance based on sensors information in industrial machines is increasingly important to avoid downtime and machine failure. Different machine learning formulations can be used to solve the predictive maintenance problem. However, many of the approaches studied in the literature are not directly applicable to real-life scenarios. Indeed, many of those approaches usually either rely on labelled machine malfunctions in the case of classification and fault detection, or rely on finding a monotonic health indicator on which a prediction can be made i…
LG Electronics（LG电子）；Advanced AI, America Research Lab（美国研究院先进人工智能实验室）
In recent times, advances in artificial intelligence (AI) and IoT have enabled seamless and viable maintenance of appliances in home and building environments. Several studies have shown that AI has the potential to provide personalized customer support which could predict and avoid errors more reliably than ever before. In this paper, we have analyzed the various building blocks needed to enable a successful AI-driven predictive maintenance use-case. Unlike, existing surveys which mostly provide a deep dive into the recent AI algorithms for Predictive Maintenance (PdM), our survey provides th…
