Measuring Machine Productivity with a Multi-Dat Data Logger: A Demonstration on Three Forest Machines

Transcription

1 Measuring Machine Productivity with a Multi-Dat Data Logger: A Demonstration on Three Forest Machines Loren Kellogg Chad Davis Chad Bolding Oregon State University Department of Forest Engineering

2 Outline of Presentation Introduction and background on Multi-dat data logger Study overview Multi-dat applications (3 machines) Forest fuels reduction and biomass utilization research Research applications & results Contractor applications & results Conclusions

3 What is the Multi-Dat? A data logger for recording productive machine time by using an internal motion sensor to detect machine vibration Productive time can be broken out by activity --- operator input or electronic signals Equipped with GPS to map machine travel Developed by FERIC; manufactured by GENEQ, Inc.

4 Purpose of Study Forest Contractor Applications Need for accurate machine productivity and delay information to improve project economics GPS technology for tracking machine travel Research Applications Shift level equipment productivity & utilization Detailed equipment cycle data

5 How the Multi-Dat Works? Internal motion sensor and/or electric sensors monitor machine motion & productivity User Input #1: Threshold value configured to ignore a certain level of machine movement User Input #2: Maximum Time Interval delineates operational delays from incidental pauses or data logger sensitivity

6 Overview of Studies Multi-Dat installed on 3 different forest machines Rubber-tired grapple skidder Excavator base masticator Cable yarder Cable yarder & masticator used motion sensor; skidder required perator input All studies relied on operator input of stop code

7 Forest Fuels Reduction; Biomass Utilization and Energy Production Research Needs Information Gaps and Research Questions? Harvesting Economics Soil Disturbance Effectiveness

8 Forest Fuels Reduction; Biomass Utilization and Energy Production Research Sample Research Questions: What are the economic differences related to the size and type of biomass harvested? What should the size and distribution of residual biomass be on-site from a fire hazard reduction effectiveness perspective?

9 Forest Fuels Reduction; Biomass Utilization and Energy Production Research What are the long-term soil compaction affects on forest soils from ground-based fuels reduction operations? What are the most cost effective harvesting and transport technologies for harvesting and delivering small logs and forest fuels for energy production --- gentle terrain ---- steep terrain --- wildland areas ---- wildland-urban interface?

10 Forest Fuels Reduction; Biomass Utilization and Energy Production Research What is the forest biomass availability for supplying energy production facilities? What opportunities are there to minimize diesel fuel usage in supplying biomass energy facilities? What are the employment creation opportunities with small log harvesting and forest biomass energy?

11 An Integrated Study of Mechanical Forest Fuels Reduction Harvesting Economics Soil Disturbance Effectiveness

12 Commercial Study Southwest OR Boise Cascade, Inc. 20-acre mixed conifer stand Slopes <12% Equipment Configuration Tracked swing-boom feller-buncher Rubber-tired skidders Processor Loader In-woods chipper

13 Non-commercial Studies Central Oregon Confederated Tribes of Warm Springs Two stands: Biddle Pass Tenino Equipment configurations Flexible track skidding machine with a front mounted mulching head Swing-boom excavator with a rotary disk mulching head

14 Soil characteristics Methods & Data Collection Pre and post-treatment treatment Soil strength Soil Disturbance Recording soil penetrometer 25-mm intervals from mm Visual disturbance Recorded by one of 12 codes using the point transect method

15 Methods & Data Collection Shift-level studies Delays >5-min were recorded by each operator Harvesting Economics Detailed time & motion studies Videotaping was used to determine the time spent performing each function of the operation

16 Methods & Data Collection Pre and post-treatment treatment Standing tree information DBH, HLC, TH, Status Understory shrub percent cover Downed woody fuel composition 1-hr, 10-hr, 100-hr, & 100-hr fuel categories Modeling with FFE-FVS FVS Effectiveness

17 Visual Results - 1

18 Visual Results - 2

19 Visual Results - 3

20 Visual Results - 4

21 Visual Results - 5

22 Back to the Multi-Dat. Rubber-Tired Skidder Research Applications Cat 518 completing a commercial fuels reduction & thinning operation Operator input of activity and delay codes Activity Codes: Outhaul, Grapple, Inhaul, Decking, Cleaning, Other Delay Codes: System Interferences, Mechanical, Maintenance, Personal, Research, Other

23 Portions of 2 shifts; 12 hours total Skidder productive time = 77% of scheduled hours Average productive skid cycle time over 137 turns was 4 minutes Delays added 1.11 minutes per skidding cycle Equipment interference at the landing was the largest delay category (10% of scheduled hours) Skidder Study Results

24 Skidder Results Continued

25 Masticator Study Results Study duration = 8 days; 75 hours Productive time = 65% of scheduled hours Delay times (% scheduled): Equipment delays = 13.7% People delays = 3.3% No Response = 9.7% End Day = 7.4%

26 100.0% 80.0% 60.0% 40.0% 20.0% 0.0% Masticator Study Results: Daily Productivity & Delay Times Daily Percentages of Time for AFE Masticator Motion Sensor Equip Delays % of Scheduled Time 28-Oct-04 3-Nov-04 4-Nov-04 5-Nov-04 8-Nov-04 9-Nov Nov Nov-04

27 Masticator Study Results: GPS capabilities Track log for masticator on 11/12/2004

28 Contractor Application: Cable Yarding Standing skyline, shotgun system Diamond 210 swing yarder and Acme slack pulling carriage Commercial thinning US Forest Service timber sale east of Sweet Home, OR Average yarding distance = 350 ft. (range ft.) 6 crew members

29 Cable Yarding Study Results 53 hours observed over 6 days Average productive time = 52 % of scheduled hours (range 42% - 65 %) Average cable road time = 27% of scheduled hours (range 7% - 44%) Average equipment interference time = 16% of scheduled hours (range 5% - 26%)

30 Research Applications Conclusions on Multi-Dat Data Logger Trials Design features limit the applicability in equipment productivity and system cost efficiency studies Capable of providing shift level information on equipment productivity and delays Number of turns and logs per turn not easily gathered in shift level studies The sensor methodology ALONE does not easily provide detail time study information Cycle element times are recorded to the nearest minute

31 Conclusions on Multi-Dat Data Logger Trials Contractor Applications

32 Contractor Applications Conclusions on Multi-Dat Data Logger Trials Provides an excellent tool for contractors to easily gather equipment productivity information over a range of conditions Allows contractors to examine results generally followed by focusing on areas of continual improvements Multi-Dat software package is easy to understand and flexible for different applications Data collection should be designed to minimize burden on equipment applications GPS tracking function provides equipment location information that can be overlain on unit maps

33 Thanks For Your Attention!