A systematic literature review of machine learning methods applied to predictive maintenance

Recently, with the emergence of Industry 4.0 (I4.0), smart systems, machine learning (ML) within artificial intelligence (AI), predictive maintenance (PdM) approaches have been extensively applied in industries for handling the health status of industrial equipment. Due to digital transformation towards I4.0, information techniques, computerized control, and communication networks, it is possible to collect massive amounts of operational and processes conditions data generated form several pieces of equipment and harvest data for making an automated fault detection and diagnosis with the aim to minimize downtime and increase utilization rate of the components and increase their remaining useful lives. PdM is inevitable for sustainable smart manufacturing in I4.0. Machine learning (ML) techniques have emerged as a promising tool in PdM applications for smart manufacturing in I4.0, thus it has increased attraction of authors during recent years. This paper aims to provide a comprehensive review of the recent advancements of ML techniques widely applied to PdM for smart manufacturing in I4.0 by classifying the research according to the ML algorithms, ML category, machinery, and equipment used, device used in data acquisition, classification of data, size and type, and highlight the key contributions of the researchers, and thus offers guidelines and foundation for further research.

Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0

Deep learning models for predictive maintenance: a survey, comparison, challenges and prospects

The traditional big-data analytical approaches use data clustering as small buckets while providing distributed computation among different child nodes. These approaches bring the issues especially concerning network capacity, specialized tools and applications not capable of being trained in a short period. Furthermore, raw data generated through IoT forming big data comes with the capability of producing highly unstructured and heterogeneous form of data. Such form of data grows into challenging task for the real-time analytics. It is highly valuable to have computational values available locally instead of through distributed resources to reduce real-time analytical challenges. This paper proposes a fusion of three different data models like relational, semantical, and big data based data and metadata involving their issues and enhanced capabilities. A case study is used to represent data fusion in action from RDB to Resource Description Framework. Whereas, issues and their feasible solutions are also being discussed in this paper.

A Methodology of Real-Time Data Fusion for Localized Big Data Analytics

Fault Detection and Diagnosis of electrical machine and drive systems are of utmost importance in modern industrial automation. The widespread use of Machine Learning techniques has made it possible to replace traditional motor fault detection techniques with more efficient solutions that are capable of early fault recognition by using large amounts of sensory data. However, the detection of concurrent failures is still a challenge in the presence of disturbing noises or when the multiple faults cause overlapping features. Multi-label classification has recently gained popularity in various application domains as an efficient method for fault detection and monitoring of systems with promising results. The contribution of this work is to propose a novel methodology for multi-label classification for simultaneously diagnosing multiple faults and evaluating the fault severity under noisy conditions. In this research, the Electrical Signature Analysis as well as traditional vibration data have been considered for modeling. Furthermore, the performance of various multi-label classification models is compared. Current and vibration signals are acquired under normal and fault conditions. The applicability of the proposed method is experimentally validated under diverse fault conditions such as unbalance and misalignment.

https://www.mdpi.com/2076-3417/9/23/5086/pdf

Fault Diagnosis of Rotating Electrical Machines Using Multi-Label Classification

In this paper, an approach to detect stator winding short-circuit faults in squirrel-cage induction motors based on Random Forest and Park's Vector is proposed. This is accomplished by scoring the unbalance in the current and voltage waveforms as well as in Park's Vector, both for current and voltage. To score the unbalance in the d-q space, a Principal Component Analysis is applied to Park's Vector and with the first two principal components the eccentricity is calculated, while the first principal component is used to determine the phase in short-circuit. The proposed strategy has been experimentally tested on a special 400-V, 50-Hz, 4-pole, 2.2-kW induction motor with reconfigurable stator windings in which it was possible to emulate different types of inter-turn short-circuits. The results are quite promising, even only using 1-kHz sampling frequency to acquire the current and voltage waveforms in the three phases, and the use of the Fast Fourier Transform is avoided. The developed solution may be used for tele-monitoring of the motor condition and to implement advanced predictive maintenance strategies.

Stator winding short-circuit fault diagnosis in induction motors using random forest

It is presented a system that allows the combination of a declarative language with a Semantic Web framework, namely, the Jena framework (https://jena.apache.org). and XSB Prolog using InterProlog [3], a library allowing the development of combined Java+Prolog applications. Our library allows RDF and SPARQL queries in Prolog predicates. In this way, we can develop Semantic Web applications that makes use of the power of declarative languages to construct sophisticated rule systems within Semantic Web environments. Benchmark results are presented, showing the practical impact of the use of the system.

RDF Query and Inference in Prolog

We present a Semantic Web based approach that meets the requirements for images retrieval based on low-level features. For that, we developed a prototypical implementation using a Semantic Web framework, linking open data ontologies and image processing libraries. We created an ontology to support the low-level features data of images. Furthermore, we created semantic rules to define concepts based on low-level features. We show how the different mechanisms of the Semantic Web may help multimedia management, both for storage and in retrieval tasks.

A Semantic Web Approach to Low-Level Features in Images

In this paper, an approach to estimate the severity of stator winding short-circuit faults in squirrel-cage induction motors based on the Cubist model is proposed. This is accomplished by scoring the unbalance in the current and voltage waveforms as well as in Park’s Vector, both for current and voltage. The proposed method presents a systematic comparison between models, as well as an analysis regarding hyper-parameter tunning, where the novelty of the presented work is mainly associated with the application of data-based analysis techniques to estimate the stator winding short-circuit severity in three-phase squirrel-cage induction motors. The developed solution may be used for tele-monitoring of the motor condition and to implement advanced predictive maintenance strategies.

Severity Estimation of Stator Winding Short-Circuit Faults Using Cubist

The time dimension is fundamental to Semantic Web applications for supporting commercial transactions or allowing the retrieval of resources contextualised in time. In this work, we propose an ontology that is an extension to the OWL-Time ontology defining non-convex intervals. We also present temporal operators for non-convex intervals and how to reason with them in our approach. We present a temporal reasoner that is able to reason both with temporal entities encoded with OWL-Time ontology and with non-convex temporal entities.

Carlos Viegas Damásio

Papers

Stator winding short-circuit fault diagnosis in induction motors using random forest

RDF Query and Inference in Prolog

A Semantic Web Approach to Low-Level Features in Images

Severity Estimation of Stator Winding Short-Circuit Faults Using Cubist

Temporal Reasoning with Non-convex Intervals