Sensors Data Analysis in Supervisory Control and Data Acquisition (SCADA) Systems to Foresee Failures with an Undetermined Origin

This paper presents the design and implementation of a supervisory control and data acquisition (SCADA) system for automatic fault detection. The proposed system offers advantages in three areas: the prognostic capacity for preventive and predictive maintenance, improvement in the quality of the machined product and a reduction in breakdown times. The complementary technologies, the Industrial Internet of Things (IIoT) and various machine learning (ML) techniques, are employed with SCADA systems to obtain the objectives. The analysis of different data sources and the replacement of specific digital sensors with analog sensors improve the prognostic capacity for the detection of faults with an undetermined origin. Also presented is an anomaly detection algorithm to foresee failures and to recognize their occurrence even when they do not register as alarms or events. The improvement in machine availability after the implementation of the novel system guarantees the accomplishment of the proposed objectives.

ANALYSIS OF MACHINE AVAILABILITY AT SURFACE-MOUNT TECHNOLOGY (SMT) LINE USING WITNESS SIMULATION

In the competition to excel in the Industrial 4.0 race, companies especially in Semiconductor Industry, strive to attain efficiency by removing process bottlenecks, improving productivity and increasing machine availability during operation. As so, this research was conducted to analyze the machine’s availability based on various preventive maintenance (PM) scheduling. A model was developed in WITNESS 14 Manufacturing Performance Edition software to imitate the Surface-mount Technology (SMT) line in a manufacturing system. The model was build based on a serial layout of five machines which are a screen printer, glue dispenser, chip shooter, pick and place and reflow oven. Then, the simulation was run to study the availability of each machine with relation to different stoppages intervals and duration based on the selected PM schedule. The findings of this project show that PM for an SMT line should be carried out every week with 30 minutes PM duration to achieve high machine availability and minimal machine downtime.

Machine availability and productivity during timber harvester machine operator training

Machine availability and timber harvest productivity in commercial forestry are influenced in part by operator performance. This work aimed to evaluate the behavior of these two variables; machine availability and productivity during the training period for the harvester operators. The study was conducted within a forestry company in Brazil. A sample of 30 individuals were trained and assessed over 11 months for their productivity and machine availability data. Monthly average data were compared using the Tukey test, in both evaluated variables. The results showed a significant difference in productivity and also in machine availability data during the training period, simultaneously presenting a productivity increase until the sixth month of operation and a decrease in machine availability. Productivity started with an average of 9 m³.PMH0-1 reaching 24 m³.PMH0-1 at its peak and stabilizing around 20 m³.PMH0-1. Machine availability started at 84%, decreased to an average of 78%, and increased to around 88% until present. Both demonstrated a tendency towards stabilization until the ninth month of operation. The harvester operator training period influenced machine availability and productivity, with this study’s results serving as important information in support of the operational planning, staff sizing, and resources during forest harvesting machine operator training period.

An integrated mathematical model for production scheduling and preventive maintenance planning

PurposeThe integration between production scheduling and maintenance planning is attracting the attention of planners in the manufacturing sector with the increase in global competitiveness. Researchers have developed various methodologies to optimize integrated decisions in planning and scheduling, including mathematical modeling under different conditions. This paper considers the simultaneous scheduling of production and maintenance activities with the objective of minimizing the expected total tardiness cost on a single machine (production line).Design/methodology/approachScheduling in these two types of activities, production and maintenance, are traditionally done independently, causing conflicts between the two functional areas. To eliminate or at least reduce conflicts, the scheduling of both activities can be done simultaneously with the objective of meeting due dates and maintaining maximum machine availability. In this paper, a mathematical model for an integrated problem is developed and demonstrated by an example.FindingsThe proposed integrated model shows a high potential for significant improvements in performance with respect to the cost of tardiness in delivery and machine availability. This is demonstrated by an example showing an average savings of approximately 40%.Originality/valueThis substantial saving is owed to the integration of two important decision-making processes in manufacturing systems. Although the integrated problem is complex and difficult to solve, the fact that it is savings driven makes the problem of interest to researchers and practitioners in manufacturing.

Predictive Maintenance in der Produktion/Predictive Maintenance within the industrial value chain

Ungeplante Maschinenausfälle führen zu Stillständen in der Produktion, die große Auswirkungen auf die Wertschöpfungskette eines Unternehmens haben. Der Einsatz von Predictive Maintenance (PdM) entlang dieser Kette erhöht die Maschinenverfügbarkeit und sichert einen reibungslosen Produktionsablauf. Im Rahmen verschiedener Forschungsprojekte am iwb werden Anwendungsfälle für PdM in der Fertigung, der Montage und der Produktionssteuerung betrachtet. Dieser Beitrag beleuchtet individuelle Herausforderungen, Lösungsansätze und Grenzen von PdM im produktionstechnischen Umfeld.   Unplanned machine downtimes can have a big impact on the value chain of a company. Predictive Maintenance (PdM) shows the potential to increase machine availability and secures smooth production processes. Different research projects at iwb examine applications of PdM within manufacturing and assembly, as well as the integration of such approaches in production planning. This article highlights individual challenges, solutions and limits of PdM within production.

Accelerator Operations

The cost of building a particle accelerator is a major capital investment. Commissioning should be swift and the subsequent exploitation of a facility must provide an effective return. This return may be difficult to quantify unambiguously but generally acceptable measures of performance can be established. These measures might include: machine availability; integrated luminosity; protons on target; beam hours to users and so on.