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opencc-by-4.0May 2016View details →

Printing Unit Condition Monitoring

This data set contains raw sensor signals of four analogue sensors and five features derived from them. These are used to monitor the condition of a printing unit in a demonstrator application and to detect a sensor defect, which is simulated in the data set. The demonstrator is used to simulate the wiping process of an Intaglio printing process. Intaglio is the major printing process to produce security prints like banknotes. Engraved structures in the printing plates, which are mounted on a rotating plate cylinder, are filled with ink, which is transferred onto the printing substrate under high pressure. A second cylinder denoted by wiping cylinder, which is working in the printing unit, is lubricated with a solvent to wipe off surplus ink from the printing plates by rotating in the direction opposite to the plate cylinder. This process is crucial as wiping errors immediately lead to print errors. The printing unit demonstrator contains models of the two cylinders, which are turned by electric drives. Pressure between the wiping cylinder having a rubber surface and the steel-surfaced plate cylinder is freely adjustable. A set of four analogue sensors (contact force, solid-bourne sound, electric current of wiping and plate cylinder drives) continuously acquire data during operation to monitor the process. The sensors each output a continuous voltage signal in the range of [-10,10] V, which is proportional to the respective quantity the sensor is observing. Thus, each signal's unit is irrelevant and abandoned as changes of the original quantity of interest are reflected also in the respective voltage signal. All output time-domain signals are synchronously and equidistantly sampled at a frequency of 20 kHz and quantised with a resolution of 16 bit. The acquired data is then split into non-overlapping batches of 50000 samples (corresponding to 2.5 sec of operation), respectively. The length of the time frame was chosen to ensure that 3 revolutions of the plate cylinder are captured in each signal data batch. The solid-bourne sound signal is treated by the FFT to determine its frequency spectrum per signal batch. Altogether, 5 features per plate cylinder revolution are extracted. This results in 15 feature values per signal data batch. The extracted features are: <ul> <li>contact force mean: arithmetic mean of the contact force,</li> <li>solid-bourne sound intensity: root mean square of the solid-bourne sound,</li> <li>solid-bourne sound maxPowFreqInd: index of the frequency component with largest power,</li> <li>motor current wiping cylinder mean: arithmetic mean of the wiping cylinder motor current,</li> <li>motor current plate cylinder mean: arithmetic mean of the plate cylinder motor current.</li> </ul> Each plate cylinder revolution is represented by one instance in the feature data sets. That is, every instance in the data set is described by a vector of 5 feature values. The raw data and feature data sets are divided into two parts, each containing data of one of the two experiments under different operation conditions: <ul> <li>Static printing unit demonstrator operation: The static experiment observes the printing unit demonstrator during 20:13 min of operation. The printing unit demonstrator was started immediately before the data acquisition began. No additional manipulations or events occurred during the experiment. Therefore, only data representing the demonstrator's normal condition is contained in the data set. It contains 10,000,000 raw signal samples resulting in 600 instances (plate cylinder revolutions), which are in summary described by 3,000 feature values.</li> <li>Manipulated printing unit demonstrator operation: The printing unit demonstrator was started ca. 23:00 min before the data acquisition began. During this 10:31 min long experiment, the demonstrator application was intentionally manipulated. In addition, the solid-bourne sound sensor signal was manipulated through low-pass filtering in order to simulate a defect of this sensor. An unintended incident also occurred during this experiment. Therefore, data representing both the demonstrator's normal and abnormals conditions are contained in the data set. The sequence of events along with an objective classification of the demonstrator condition by the human experimenter is summarised in the file PrintingUnit_manip_events.txt. The data set contains 5,950,000 raw signal samples, which are in summary described by 1,785 feature values.</li> </ul> File name conventions and contents The files in the data set are organised such that each row represents a data set instances, columns represent the respective sensor or feature: <ul> <li>PrintingUnitData*.csv: These files contain raw sensor signals.</li> <li>PrintingUnitFeatures*.csv: These files contain the features extracted from the sensor signals.</li> </ul> Additional files contain information about <ul> <li>*_condition.csv: The condition if the printing unit is indicated by 'n' (normal condition) or 'a' (abnormal condition). These are the labels of the data set instances.</li> <li>*_filter.csv: The solid-bourne sound filter status is indicated by '1' (filter activated) or '0' (filter deactivated).</li> <li>*_time.csv: The relative time, at which the respective instance of the data set was determined. It is represented as the number of days from January 0, 0000 as is returned from MATLAB's datenum function (cf. http://www.mathworks.com/help/matlab/ref/datenum.html for details).</li> </ul> The operation conditions (with respect to the experiment, cf. above) are distinguished by <ul> <li>*_static*.csv: Static printing unit demonstrator operation.</li> <li>*_manip*.csv: Manipulated printing unit demonstrator operation.</li> </ul>

opencc-by-4.0Feb 2019View details →

Operating diagram of hatching module, this module consists of two clearly separated sections, each consisting of two long tanks (2 × 0.2 × 0.2 m) designed to accommodate hatching boxes, a filtration tank and an independent water circulation pump with a cooling unit and UV sterilizer. This allows simultaneous monitoring of 16 batches of eggs. in Reproduction of Zingel asper (Linnaeus, 1758) in controlled conditions: an assessment of the experiences realized since 2005 at the Besançon Natural History Museum

Operating diagram of hatching module, this module consists of two clearly separated sections, each consisting of two long tanks (2 × 0.2 × 0.2 m) designed to accommodate hatching boxes, a filtration tank and an independent water circulation pump with a cooling unit and UV sterilizer. This allows simultaneous monitoring of 16 batches of eggs.

opencc-by-4.0Feb 2019View details →

Bottom of DR1 tank, the sides of the module are fitted with glass panels which allow natural light from a window to enter the tank, and the observer to view the behaviour of the broodstock. This device ensures easy viewing and checking of the broodstock, facilitates management of feeding and allows effective monitoring of reproduction. in Reproduction of Zingel asper (Linnaeus, 1758) in controlled conditions: an assessment of the experiences realized since 2005 at the Besançon Natural History Museum

Bottom of DR1 tank, the sides of the module are fitted with glass panels which allow natural light from a window to enter the tank, and the observer to view the behaviour of the broodstock. This device ensures easy viewing and checking of the broodstock, facilitates management of feeding and allows effective monitoring of reproduction.

opencc-by-4.0Nov 2011View details →

Figure 7. The outcomes of the Monitoring and Control of the Greenhouse soil and climate Conditions for tomato crops-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System

In the past generation greenhouses it was enough to have one cabled measurement point in the middle to provide the information to the greenhouse automation system. The system itself was usually simple without opportunities to control locally heating, lights, ventilation or some other activity, which was affecting the greenhouse interior climate. The optimal greenhouse climate and soil adjustment can enable us to improve productivity and to achieve remarkable energy savings. In this paper we proposed a multi-agent methodology for integrated management systems in greenhouses. In this regards wireless sensor networks play a vital role to monitor greenhouse and environment parameters. Each controlled process of the greenhouse environment is modeled as an autonomous agent with its own inputs, its own outputs and its own interactions with the other agents. Each agent acts autonomously, as it knows a priori the desired environmental set-points. In this way, any possible conflicting decisions of conventional environmental control methodologies are resolved through negotiations between the agents so that the possible optimal integrated solution is achieved. The developed system is simple, cost effective, and easily installable.

Figure 6. Structure of the MAS for integrated greenhouse management system.-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System

The complete agents “environment” is shown in figure 6. Each agent receives the necessary environmental or plant condition measurements. While each agent keeps its autonomy and has its own “personal” goals, it interacts with the other agents through some specific communication language, in the “Agent discussion area”, where the overall goal is taken into account. This interaction takes place under the presence of information from the existing models and the target goals. At that point, each agent has decided on its best strategy.

Figure 2. Experimental wireless sensors setup in greenhouse-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System

Figure 2 illustrates how the sensor nodes were deployed to the greenhouse block. The idea of the vertical deployment was to get a better understanding of the microclimate layers which typically exist in the greenhouse, and to figure out what kind of differences occur in the climate between lower and upper flora.

Figure 5. Multi-agent system information and knowledge scheme.-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System

The negotiations between agents are subject to optimization based on “knowledge” that is derived from complete production models, yield models or even sparse models as expressed in fuzzy expert rules or practical rules of thumb. In addition, pest control and plant disease models provide additional information useful to the design of a successful strategy for optimal management [15] (illustrated in Figure 5).

Figure 1. Agent Architecture-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System

The multi agent method creates a non-structured environment using agents, these agents, in order to reach the global optimum, must be capable of performing transactions between each other, achieving several “deals”, which is performed though a common vocabulary, a finite set of information exchange, a finite set of possible actions, penalties, etc. The power of each agent depends on the degree of contribution of its represented process to the final output of the entire system. Each agent communicates with the environment and adapts to its own internal state as well as to the state of the entire system. In this systems, for the realization of the multi agent architecture the JADE 4.1.1 (Java Agent DEvelopment Framework) have been used [7].

Figure 4. The multi-agent overall environment for integrated management-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System

The value of the final product incorporates not only quantity issues but also quality issues, which are difficult to be measured or even estimated. The environment of each agent is defined by the same parameters that define the physical environment in addition to internal states reported by each agent.

Figure 3. Photosynthetic activity in different wavelengths of light radiation. 5.-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System

The greenhouse protects the plants from the extreme weather conditions. However, if the period of daylight prevents the photosynthetic activity, the plants do not grow. Horticultural lighting allows the grower to extend the growing season. It enables a year-round producing of plants or makes it possible for the grower to start sowing in early spring and continue season till the first frost. Plants need about 10-12 hours light to improve growth. When the plants are producing flowers or fruits the supplemental need of light per day increases up to 16 hours. Figure 3 shows the photosynthetic activity in different wavelengths of light radiation [16].

opencc-by-4.0Jun 2024View details →

Wind turbine condition monitoring dataset of Fraunhofer LBF

<h3>Fraunhofer wind turbine dataset  contains monitoring data from a 750 W wind turbine (WT), including accelerometers and tachometer, to capture structural response, bearing vibrations and rotational velocity. Additionally, temperatures of the structure, wind speed and wind direction have been measured, while weather conditions have been acquired from selected sources. Various damage scenarios, including mass imbalance, and aerodynamic imbalance as well as damages on bearings’ outer race, inner race and roller element have been implemented. The availability of time series data makes the dataset well suited for both machine learning and signal processing-based condition monitoring (CM) applications. The availability of heterogeneous sensors has created a dataset particularly suited for information fusion, data fusion, multi-sensor approaches, and holistic monitoring. Experiments were conducted in real-world conditions outside of a controlled laboratory environment, thereby introducing challenges such as variable rotor speed, noise, overloads, and other environmental factors. Consequently, the dataset is qualified for tasks involving uncertainty quantification and signal pre-processing. This document will detail the test equipment, experimental procedures, simulated damage cases, measurement parameters, data specifics, and preliminary analysis aimed at validating data quality.</h3> <h3>See the full data descriptor at: https://doi.org/10.1038/s41597-024-03934-5</h3>

opencc-by-4.0Sep 2020View details →

Implementation of ATP and Microbial Indicator Testing for Hygiene Monitoring in a Tofu Production Facility Improves Product Quality and Hygienic Conditions of Food Contact Surfaces: A Case Study

This is the code and associated data that was used to generate conclusions for the following manuscript published in Applied and Environmental Microbiology: DOI: 10.1128/AEM.02278-20 Implementation of ATP and Microbial Indicator Testing for Hygiene Monitoring in a Tofu Production Facility Improves Product Quality and Hygienic Conditions of Food Contact Surfaces: A Case Study Authors: Jonathan H. Sogin(a), Gabriela Lopez Velasco(b), Burcu Yordem(b), Cari K. Lingle(b), John M. David(b), Mario Cobo(a), Randy W. Worobo(a) (a)Department of Food Science, Cornell University, Ithaca, NY, USA (b)3M Company, St. Paul, MN, USA Address correspondence to Jonathan H. Sogin, jhs397@cornell.edu

opencc-by-4.0Apr 2018View details →

Condition monitoring of hydraulic systems Data Set at ZeMA

Abstract: The data set addresses the condition assessment of a hydraulic test rig based on multi sensor data. Four fault types are superimposed with several severity grades impeding selective quantification.   Source: Creator: ZeMA gGmbH, Eschberger Weg 46, 66121 Saarbrücken Contact: t.schneider '@' zema.de, s.klein '@' zema.de, m.bastuck '@' lmt.uni-saarland.de, info '@' lmt.uni-saarland.de   Data Set Information: The data set was experimentally obtained with a hydraulic test rig. This test rig consists of a primary working and a secondary cooling-filtration circuit which are connected via the oil tank [1], [2]. The system cyclically repeats constant load cycles (duration 60 seconds) and measures process values such as pressures, volume flows and temperatures while the condition of four hydraulic components (cooler, valve, pump and accumulator) is quantitatively varied.   Attribute Information: The data set contains raw process sensor data (i.e. without feature extraction) which are structured as matrices (tab-delimited) with the rows representing the cycles and the columns the data points within a cycle. The sensors involved are: Sensor Physical quantity Unit Sampling rate PS1 Pressure bar 100 Hz PS2 Pressure bar 100 Hz PS3 Pressure bar 100 Hz PS4 Pressure bar 100 Hz PS5 Pressure bar 100 Hz PS6 Pressure bar 100 Hz EPS1 Motor power W 100 Hz FS1 Volume flow l/min 10 Hz FS2 Volume flow l/min 10 Hz TS1 Temperature °C 1 Hz TS2 Temperature °C 1 Hz TS3 Temperature °C 1 Hz TS4 Temperature °C 1 Hz VS1 Vibration mm/s 1 Hz CE Cooling efficiency (virtual) % 1 Hz CP Cooling power (virtual) kW 1 Hz SE Efficiency factor % 1 Hz The target condition values are cycle-wise annotated in ‘profile.txt’ (tab-delimited). As before, the row number represents the cycle number. The columns are 1: Cooler condition / %: 3: close to total failure 20: reduced effifiency 100: full efficiency 2: Valve condition / %: 100: optimal switching behavior 90: small lag 80: severe lag 73: close to total failure 3: Internal pump leakage: 0: no leakage 1: weak leakage 2: severe leakage 4: Hydraulic accumulator / bar: 130: optimal pressure 115: slightly reduced pressure 100: severely reduced pressure 90: close to total failure 5: stable flag: 0: conditions were stable 1: static conditions might not have been reached yet   Relevant Papers: [1] Nikolai Helwig, Eliseo Pignanelli, Andreas Schütze, ‘Condition Monitoring of a Complex Hydraulic System Using Multivariate Statistics’, in Proc. I2MTC-2015 - 2015 IEEE International Instrumentation and Measurement Technology Conference, paper PPS1-39, Pisa, Italy, May 11-14, 2015, doi: 10.1109/I2MTC.2015.7151267. [2] N. Helwig, A. Schütze, ‘Detecting and compensating sensor faults in a hydraulic condition monitoring system’, in Proc. SENSOR 2015 - 17th International Conference on Sensors and Measurement Technology, oral presentation D8.1, Nuremberg, Germany, May 19-21, 2015, doi: 10.5162/sensor2015/D8.1. [3] Tizian Schneider, Nikolai Helwig, Andreas Schütze, ‘Automatic feature extraction and selection for classification of cyclical time series data’, tm - Technisches Messen (2017), 84(3), 198 – 206, doi: 10.1515/teme-2016-0072.   Citation Request: Nikolai Helwig, Eliseo Pignanelli, Andreas Schütze, ‘Condition Monitoring of a Complex Hydraulic System Using Multivariate Statistics’, in Proc. I2MTC-2015 - 2015 IEEE International Instrumentation and Measurement Technology Conference, paper PPS1-39, Pisa, Italy, May 11-14, 2015, doi: 10.1109/I2MTC.2015.7151267.

opencc-by-4.0Jun 2023View details →

Monitoring of postpartum body condition at the cow and herd levels: assessing explanatory and predictive power of disease risk models

Objectives 1- To define the herd threshold for cows with poor body condition based on its predictive capacity for disease risk at the herd level, and 2- to estimate the impact measures on disease rates due to body condition indicators in transition period. Two commercial grazing dairy herds (Herd A=5.034 and herd B=7.965 lactations) from Argentinean Pampa region were used to perform a longitudinal retrospective study during a 4-year period (2014 –2017).Health, reproductive and body condition score (BCS) records were gathered. The BCS (5-point scale) was performed at calving and at the time of reproductive release. The difference between both measures of BCS was used to assess the body condition loss (∆BCS). All the cows not bred by 70 DIM were checked for anestrus.Calving cohorts of 21-day were defined at each herd and parity group through the entire study period. The frequency of cows with BCS<3 or ∆BC>-0.5 at each cohort were calculated and used to define quartiles through whole study period. Quartiles were used, one at a time, as threshold to dichotomize the cohorts to predict the risk that a cohort has a frequency of anestrus over the median.The higher AUC was used as selection criterium to determine the herd level threshold at each HERD and PARITY level. The population attributable fraction (AFP) of anestrus rate to body condition indicators at each cohort was calculated, for every HERD and PARITY level.

opencc-by-4.0Jul 2023View details →

Design Of A Condition Monitoring System (CMS) For A Gas Compressor Using Shell Bonny Terminal As A Case Study

Subcontracted Researcher & Field Electrical and Electronics Engineer, Shell Bonny Terminal, Nigeria— 2013 I served as a Hooks Electric subcontracted researcher and field electrical and electronics engineer at Shell Bonny Terminal, spearheading a groundbreaking project to design and construct a state-of-the-art software condition monitoring system (CMS) for a gas compressor. In addition to my technical responsibilities, I also took charge of integrating this innovative design into the existing SCADA (Supervisory Control and Data Acquisition) infrastructure of the plant. Working collaboratively with a team of four talented engineers, I successfully managed and coordinated their efforts throughout the project. Together, we leveraged our collective expertise to develop and implement a fully functional software condition monitoring system. This collaborative approach ensured that our project benefitted from diverse perspectives and skill sets, leading to a comprehensive and robust solution. Utilizing my proficiency in LabVIEW, C#, native C, Java and ActiveX library, I led the development of a cutting-edge data acquisition (DAQ) device for CM system. This device seamlessly captured sensor readings from the live gas compressor in real-time, while the systemic algorithm I designed skillfully tracked false alarms using fallback sensors. By monitoring critical parameters such as ambient and self-temperature, axial speed, vibration, inflow pressure, and outflow pressure, we were able to enhance the system's reliability and performance. Furthermore, I effectively integrated the software condition monitoring system into the plant's existing SCADA infrastructure, ensuring seamless communication and compatibility with the overall plant monitoring and control system. This integration facilitated centralized data management and provided comprehensive insights into the gas compressor's operation, enabling proactive maintenance and efficient decision-making. By successfully managing the team and overseeing the integration process, I ensured that our project aligned seamlessly with the plant's existing infrastructure, resulting in an optimized and harmonized system. Through our collective efforts, we delivered a highly sophisticated and fully integrated software condition monitoring system that significantly improved the management and performance of the gas compressor at Shell Bonny Terminal. I have curated all the resource files that will enable anyone replicate our work. This is shared under the CC-by-4 license. The LabVIEW codes are documented in Work.zip file and all manuals and equipment specifications have been uploaded. I have created a test bench to help anyone quickly test the LabVIEW software in simulation mode.  I have also provided a slide presentation that summarizes our work, including methodologies and results.