Energy Consumption Data
Dataset ID: PowerConsumptionofBeltConveyors1
Dataset Title: Power Consumption of Belt Conveyors
Dataset Description:
The testbed used for this data collection (henceforth denoted as FASTory) was previously used in a real factory for assembly of mobile phone components. Figure 1 illustrates the layout of FASTory. The line was retrofitted to simulate its original operations (assembly of Frame, Keyboard and Screen components). The testbed comprises of ten workstations, one static buffer cell, and one loading / unloading station. Each workstation includes one main conveyor (main CNV), one bypass conveyor (bypass CNV), and one robot e.g. SCARA robot (SONY SRX-611).
The conveyor system of each cell (Figure 1) consists of one bypass (Capacity: One pallet) and one main conveyor (Capacity: Two pallets). The main conveyor includes 4 stoppers (stoppers at Zone 1, Zone 2, Zone 3, and Zone 5) and the bypass conveyor, one stopper (Zone 4). NFC readers installed beside each stopper, collect information regarding completed operations from the NFC tags carried by the pallets.
Figure 1: FASTory Line Workstation
The FASTory line is coordinated by a hybrid methodology, using client-server and peer to peer paradigms. The system is composed of the physical devices in the line and a Decision Support System (DSS) located in an external computer. The devices and the DSS functionality are exposed as Web Services. The devices can communicate peer-to-peer and with the DSS. All invocations and notifications are Web Service based. Exposed REST event notifications include information about energy consumption (via S1000 energy meters, E10 module) and pallet input to a conveyor piece.
All FASTory cells are equipped with energy meters integrated into S1000 processing units (Smart Remote Terminal Units). Each energy meter is an E10 Energy Analyzer expansion module which provides 3-phase electrical power consumption monitoring (Fig. 2). Phase A is consigned to the robot, Phase B is allocated to the cabinet, I/O and the controller and Phase C is assigned to the conveyor system including the main and bypass conveyor. Power measurement is achieved by sampling current and voltage.
Figure 2 depicts the current sampled by a current transformer (CT) connected to +Ia-, +Ib- and +Ic- terminals and the voltage is measured by direct connection of the 3 phases and neutral to the Vn, Va, Vb and Vc terminals of the E10 expansion module. Equipment workload refers to the number of pallets occupying the conveyor at one time. To monitor this information, inductive sensors are mounted at the entrance and exit points of each cell, and REST TransferIn/TransferOut notifications coming from the line are counted.
Figure 2: Settings used to monitor energy consumption
Workstation 9 (WS9) is used for the collection of power consumption data under different load conditions i.e. from no load (no pallets) to full load (4 pallets: 3 at main and 1 at bypass CNV). Data is collected at a 1 second sampling rate by running both conveyors irrespective of whether the pallets are residing on them and stopped via stoppers or not. When stoppers are in use, there is an increase in friction between the conveyor belt and the pallet, which results in an observed increase of power consumption in the conveyor motor driver. The data is used for power-based detection of gradual deterioration of expected behavioural changes in the considered equipment piece i.e. “conveyor belt gradual tension degradation” in the current case. As the conveyor belts are the workhorse of production industries and their tension play an important role in production industries for material transportation from one place/workstation to another. The belt health and material transportation time are heavily affected by the belt tension. If there is less tension in the belt (i.e. below the nominal tension), then the belt sags and slips, which leads to an increase in the material transportation time.
Dataset Keywords:
Power Consumption data, anomaly detection, predictive maintenance, data analysis, 3-phase Energy data
Dataset Keywords:
Power Consumption data, anomaly detection, predictive maintenance, data analysis, 3-phase Energy data
Data Provider Name:
Tampere University (FAST-Lab)
Data Provider Country:
Finland
Dataset Update Frequency:
No update. Data from a fixed period will be only available.
Dataset Size:
A .csv file containing power consumption of WS9 conveyor data of 1-day period has about 192KB size and 3900 records.
Number of Attributes:
10 (workstation ID, RMS current, RMS Voltage, Power factor, Active Energy (W-Hr), Active Energy (KW-Hr), Power (W), Power (KW), Label (number of pallets on CNV at a time) and Class (0: 0-1 pallets; 1: 2-4 pallets).
Data Format and Storage:
Data is transmitted from E10 module in a raw format and stored in a SQLite DB. The data will be exported as a .csv file.
Data Attributes:
Workstation ID, RMS current, RMS Voltage, Power factor, Active Energy (W-Hr), Active Energy (KW-Hr), Power (W), Power (KW), Label (number of pallets on CNV at a time) and Class (0: 0-1 pallets; 1: 2-4 pallets).
Personal Data:
Not present
Level of Aggregation:
Preliminary aggregation from raw data to a .csv file.