Evaluation of Change Point Detection Algorithms for Application in Big Data Mini-term 4.0

Garcı́a, E.; Montés, Nicolás; Llopis, Javier; Lacasa, Antonio

doi:10.5220/0009594001170124

Cited by 3 publications

(4 citation statements)

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“…A comparison was made, both in its effectiveness and precision in detecting the change point, as well as in its computation time. From this comparison, the result showed that Bartlett's algorithm was the most effective one in which the version of the winding sliding algorithm is the most efficient at computational level [24]. This algorithm was implemented in the system, replacing the previous one.…”

Section: Towards Robust Detection Of the "Change Point" Of Mini-termmentioning

confidence: 98%

“…As explained in the previous section, the fault detection algorithm has undergone different evolutions. Initially, an algorithm based on K-Means was developed [21], which made it possible to collect cases of faults and carry out a more in-depth study of change point detection algorithms, see [24], where the conclusion was that Bartlett's algorithm was the most suitable one. However, this algorithm still had problems when the anomaly was fluctuating, or slow, and also detected the Scan-Time as an anomaly.…”

Section: Effectiveness Of the Detection Algorithmmentioning

confidence: 99%

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Miniterm, a Novel Virtual Sensor for Predictive Maintenance for the Industry 4.0 Era

García¹,

Sánchez

Llopis³

et al. 2022

Sensors

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This article introduces a novel virtual sensor for predictive maintenance called mini-term. A mini-term can be defined as the time it takes for a part of the machine to do its job. Being a technical sub-cycle time, its function has been linked to production. However, when a machine or component gets deteriorated, the mini-term also suffers deterioration, allowing it to be a multifunctional indicator for the prediction of machine failures as well as measurement of production. Currently, in Industry 4.0, one of the handicaps is Big Data and Data Analysis. However, in the case of predictive maintenance, the need to install sensors in the machines means that when the proposed scientific solutions reach the industry, they cannot be carried out massively due to the high cost this entails. The advantage introduced by the mini-term is that it can be implemented in an easy and simple way in pre-installed systems since you only need to program a timer in the PLC or PC that controls the line/machine in the production line, allowing, according to the authors’ knowledge, to build industrial Big Data on predictive maintenance for the first time, which is called Miniterm 4.0. This article shows evidence of the important improvements generated by the use of Miniterm 4.0 in a factory. At the end of the paper we show the evolution of TAV (Technical availability), Mean Time To Repair (MTTR), EM (Number of Work order (Emergency Orders/line Stop)) and OM (Labour hours in EM) showing a very important improvement as the number of mini-terms was increased and the Miniterm 4.0 system became more reliable. In particular, TAV is increased by 15%, OM is reduced in 5000 orders, MTTR is reduced in 2 h and there are produced 3000 orders less than when mini-terms did not exist. At the end of the article we discuss the benefits and limitations of the mini-terms and we show the conclusions and future works.

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Section: Towards Robust Detection Of the "Change Point" Of Mini-termmentioning

confidence: 98%

Section: Effectiveness Of the Detection Algorithmmentioning

confidence: 99%

Miniterm, a Novel Virtual Sensor for Predictive Maintenance for the Industry 4.0 Era

García¹,

Sánchez

Llopis³

et al. 2022

Sensors

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“…Penalty methods were not used to determine surgical time change points. The "normal" method was used as the test statistic for the surgical time 10 .…”

Section: Learning Curve Analysismentioning

confidence: 99%

The learning curve for pure retroperitoneoscopic donor nephrectomy by using cumulative sum analysis

Mercimek,

Ozden,

Gulsen

et al. 2023

CUAJ

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Introduction: This study aimed to identify a precise learning curve for pure retroperitoneoscopic donor nephrectomy (RDN). Methods: Data from 172 consecutive kidney donors who underwent pure RDN between January 2010 and July 2019 were prospectively collected and evaluated. CUSUM (cumulative sum) analysis was used for testing the operation time. Changepoints were determined by using the r program and Binseg method. The cohort was divided into three groups — group 1: competence, including the first 10 cases; group 2: 11–48 cases as proficiency; and group 3: the subsequent 124 cases as expert level. Continuous variables were evaluated using one-way ANOVA, and categorical data were evaluated using the Chi-squared test. Results: Right RDN was performed in 39 (22.7%) donors. The eighth patient was converted to open surgery due to vena cava injury and excluded from the CUSUM analysis. Depending on experience in pure RDN, a significant decrease was detected in operative time (p<0.001), warm ischemia time (p=0.006), and blood loss (p<0.001). Recipient complications and graft function were found to be statistically comparable. Conclusions: In our study, the attainment of expertise in pure RDN was observed after performing 50 cases. The transperitoneal technique, which is a feasible alternative, is far more widely used than pure RDN. We believe that understanding the learning curve associated with pure RDN could facilitate the adoption of this approach as a viable alternative to the transperitoneal approach.

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“…In 2019, the company took part in a project funded by the Centre for the Development of Industrial Technology (CDIT), IDI-20190878, a public business entity dependent on the Ministry of Science and Innovation, that promotes innovation and technological development by Spanish companies. This project made it possible to implement mini-terms at the Ford Factory in Almussafes (Valencia) on a large scale and to develop the necessary algorithms for the detection of machine failures [21]. As of today, thousands of components are being monitored at the factory in Valencia through mini-terms, with a high success rate in the early detection of component failures (see [19]).…”

Section: Mini-term 40 Installation Setupmentioning

confidence: 99%

Manufacturing Maps, a Novel Tool for Smart Factory Management Based on Petri Nets and Big Data Mini-Terms

Llopis¹,

Lacasa²,

García³

et al. 2022

Mathematics

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This article defines a new concept for real-time factory management—manufacturing maps. Manufacturing maps are generated from two fundamental elements, mini-terms and Petri nets. Mini-terms are sub-times of a technical cycle, the time it takes for any component to perform its task. A mini-term, by definition, is a sub-cycle time and it would only make sense to use the term in connection with production improvement. Previous studies have shown that when the sub-cycle time worsens, this indicates that something unusual is happening, enabling anticipation of line failures. As a result, a mini-term has dual functionality, since, on the one hand, it is a production parameter and, on the other, it is a sensor used for predictive maintenance. This, combined with how easy and cheap it is to extract relevant data from manufacturing lines, has resulted in the mini-term becoming a new paradigm for predictive maintenance, and, indirectly, for production analysis. Applying this parameter using big data for machines and components can enable the complete modeling of a factory using Petri nets. This article presents manufacturing maps as a hierarchical construction of Petri nets in which the lowest level network is a temporary Petri net based on mini-terms, and in which the highest level is a global view of the entire plant. The user of a manufacturing map can select intermediate levels, such as a specific production line, and perform analysis or simulation using real-time data from the mini-term database. As an example, this paper examines the modeling of the 8XY line, a multi-model welding line at the Ford factory in Almussafes (Valencia), where the lower layers are modeled until the mini-term layer is reached. The results, and a discussion of the possible applications of manufacturing maps in industry, are provided at the end of this article.

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Evaluation of Change Point Detection Algorithms for Application in Big Data Mini-term 4.0

Cited by 3 publications

References 0 publications

Miniterm, a Novel Virtual Sensor for Predictive Maintenance for the Industry 4.0 Era

Miniterm, a Novel Virtual Sensor for Predictive Maintenance for the Industry 4.0 Era

The learning curve for pure retroperitoneoscopic donor nephrectomy by using cumulative sum analysis

Manufacturing Maps, a Novel Tool for Smart Factory Management Based on Petri Nets and Big Data Mini-Terms

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