SIMPRO TUT SP2 Utilization of Simulation Data to Support the Maintenance of Mobile Work Machines

Transcription

1 SIMPRO TUT SP2 Utilization of Simulation Data to Support the Maintenance of Mobile Work Machines Petteri Multanen, Tomi Krogerus, Jukka-Pekka Hietala Mika Hyvönen and Kalevi Huhtala Tampere University of Technology Dept. of Intelligent Hydraulics and Automation (IHA)

2 Introduction Presentation is based on Tampere Univ. of Technology s Sub Project 2 in SIMPRO: Utilization of simulation data to support the maintenance of mobile work machines Diagnostics of mobile work machines is challenged by: Limited amount of sensors due to relatively low cost of machines Harsh and highly varying operating conditions and operators General problematic related to data analysis and reasoning Simulation models and simulators are necessity in the development of modern, highly automated machines. Project goals were: Develop procedure and methods for using simulation models and simulators to support the diagnostics and maintenance of machines. Develop tools and algorithms for feature recognition and for recognition of machine state and condition. Test and evaluate the tools and algorithms with real machines.

3 Studied Mobile Work Machines Autonomous mobile work machines and their simulators were used at IHA as test machines. The frames of the machines are original, but control system, sensors, electronics and hydraulics have optimised for autonomous and remote controlled operation. GIM-machine, Avant Tecno s wheel loader. HIL simulator and real machine were used for initial testing of analysis algorithms. IHA-machine, Wille wheel loader. Field tests with real machine. GIM-machine IHA-machine

4 Dynamic Mathematical Models and HIL Simulator (1)

5 Dynamic Mathematical Models and HIL Simulator (2) The simulation models included the following parts and properties of work machines: Mechanics, machine body and tyre-road interaction Hydrostatic drive Work hydraulics and fluid characteristics Dynamic friction models Diesel engine Numerous sensors Models were verified with several lab and field measurements Hydraulic circuit of hydrostatic drive and flushing valve (grey).

6 n [10 r/min];disp. [%] v [km/h]; Cons. [kg/h] p [bar] Verification Example for Simulation Models 50 different acceleration and deceleration tests Signal Range Unit Diesel engine rotational speed rpm HSD pump displacement % Machine velocity 0 20 km/h Consumption of diesel engine 0 20 kg/h Pressure at port A bar Pressure at port B bar n meas v meas p Ameas 150 ep meas em meas 15 cons meas v sim 400 p Bmeas p Asim 100 n sim ep sim em sim 10 cons sim p Bsim Time [s] Time [s] Time [s]

7 Maintenance Procedure Utilizing HIL Simulators

8 Analysis of Time Series data

9 Experiments Autonomous wheel loader was used for the testing of data analysis. Only 4 variables 41 test drives; 20 drives were used in the training phase, i.e. for statistical model generation, and 21 drives were used in the actual testing phase. Machine fault was a jammed flushing valve in hydrostatic transmission.

10 Pressure B [bar] Segmentation Each data set contained 800 data points for each measured variable The measurements were segmented into parts of the same length The length of the segment was 100 with 50 overlapping data points Segment no. 5 Segment no. 4 Segment no. 3 Segment no. 2 Segment no Data points Data points

11 Segments Correlation Coefficients and Histograms 4 simulated and 4 measured variables PDFs (Probability density functions) for correlation coefficients were computed and presented using histograms Histogram interval [-1, 1] was divided into 21 bins -> Model of undamaged healthy machine Pearson s correlation coefficients for data sets x i and x k Correlation coefficient

12 Logarithm of joint probability Experiments - Results Detection based on static threshold and arithmetic mean of joint probability distribution Simulated vs Real undamaged (Train) Simulated vs Real undamaged (Test) Simulated vs Real damaged (Test) Mean(undamaged train) = Mean(undamaged test) = Mean(damaged test) = Threshold = Segments p i,k, the number of times that r i,k j : j = 1,, N falls in each bin is counted and normalized such that sum of p i,k over all bins equals 1

13 Subproject Deliverables Utilization of R&D simulation models at the maintenance of mobile ma-chines (In Finnish). BSc thesis. C. Oksman. 2013/8. Report on methods, procedures and analysis tools. SIMPRO project report, TUT Research report. J-P. Hietala et al. 2014/1. Anomaly Detection and Diagnostics of a Wheel Loader Using Dynamic Mathematical Model and Joint Probability Distributions. Conference article. T. Krogerus et Al. The 14th Scandinavian International Conference on Fluid Power. May 20 22, 2015, Tampere, Finland. 14 p. Novel Procedure for Supporting Maintenance of Mobile Work Machines Using R&D Simulators. Conference article. J-P. Hietala et Al. The 11th Int. Conf. on Condition Monitoring and Machinery Failure Prevention Technologies, June 2014, Manchester, UK. 9 p. Joint probability distributions of correlation coefficients in the diagnostics of mobile work machines. Journal article in Review. T. Krogerus et Al. Elsevier's Journal of Mechatronics, The Science of Intelligent Machines. Diagnostics of Mobile Work Machines Using Dynamic Mathematical Mod-els and Joint Probability Distributions. Seminar poster. P. Multanen et Al. SIMPRO Final seminar Utilization of Simulation Data to Support the Maintenance of Mobile Work Machines. Research report, part of the SIMPRO Final report. P. Multanen. et Al. 2015/10.

14 Conclusions TUT SP2 developed a procedure for using simulators and simulation models for the diagnostics of machines and to support the maintenance work on the field. Essential part of was the selection and testing of analysis tools for the recognition of machine condition. In joint probability distribution method the probabilities of multiple correlation coefficients are compared instead of comparing correlations directly. This enables the detection of anomalies, rare situations with low probabilities, from which one can conclude if there is something wrong in the system. Analysis method was applied to the diagnostic of autonomous mobile work machines. A jammed flush valve in the hydrostatic drive of wheel loader was presented as a test case. Test results showed clearly lower probabilities for test drives where fault was present. Analysing methodology enables the detection of sudden critical faults as well as slowly evolving failures. In the case studies the machines were autonomous hydraulically driven mobile work machines and their operating behaviour was compared to the responses of Hardware-in-the-loop simulator. However, the use of maintenance procedure and analysis algorithms are applicable to many other machine systems and environments. Also the generation of simulation data does not require real time simulation or the use of hardware components of machines as long as the simulated responses correspond the behaviour of real machine. TUT s work on this field of research has already continued and the results are utilized in other projects.