Robust Magnetic Sensors for availabilityoriented. Dr. Rolf Slatter, Sensitec GmbH, Lahnau

Transcription

1 Robust Magnetic Sensors for availabilityoriented Product-Service-Systems Dr. Rolf Slatter, Sensitec GmbH, Lahnau

2 Agenda From products to solutions and product-service systems Smart xmr sensors for condition monitoring Use cases Conveyor belt monitoring in a potato harvester Knife sharpness monitoring in a tobacco cutter Conclusions and outlook 2

3 From products to solutions and product-service systems Product-service systems (PSS) are business models that provide for cohesive delivery of products and services PSS can provide the basis for higher profits for machine builders and/or offer environmental benefits Main categories of product-service system [Tukker & Tischner, 2006] Already a classic [Baines & Lightfoot, 2013] 3

4 Successful examples* of Product-Service Systems, but. Rolls-Royce's Power-by-the-Hour service package for aircraft engines, whereby maintenance, repair and overhaul services are charged per hour of flight Atlas Copco's Contract Air service, whereby air compressors are sold per m³ of compressed air delivered Philips' pay-per-lux model for selling lighting equipment, whereby customers pay for a promised level of illuminance in a building Michelin's fleet management solution whereby truck tires are sold per kilometre driven *Source: Wikipedia 4

5 Obstacles to the growth of product-service systems Existing and growing demand for guaranteed availability in the capital-goods market However, many companies (SMEs) are reluctant to offer such guarantees: Missing operational data and insufficient transparency regarding the condition of the capital goods during usage Inadequate basis for condition-based maintenance and the associated business models The resultant cost (risk) is not economically acceptable either for the manufacturer and/or for the user The further development of product-service systems and individualised service products depends strongly on the increased availability of field data by means of smart sensors 5

6 Project InnoServPro Consortium Project Duration: bis Funding: State funding: ca. 3,9 Mio. Total project volume: ca. 7,1 Mio. Project sponsored by: 6

7 Use case: Conveyor belt monitoring in a potato harvester Wear (due to tough environment) Maintenance and replacement Early (Inspection) Too late (Repair) Lifetime not fully utilised Failure Grimme Varitron 470 Potato Harvester Costs therefore Sensors for monitoring of condition thereby Maintenance and replacement before failure Improvement in availability and quality Conveyor belt for harvested potatoes New PSS Business Models 7

8 Use case: Conveyor belt monitoring in a potato harvester 8

9 Use case: Conveyor belt monitoring in a potato harvester Fault tree analysis revealed the conveyor belt to be a major cause of down time The conveyor belt carries the harvested potatoes into the machine to be cleaned and sorted The conveyor belt comprises several parallel transport belts connected by metal bars spaced every 45 mm along the conveyor belt Individual belt segments are connected by metal hinges Objective: Achieve the best compromise between maximum belt life and belt replacement before failure 9

10 Wear mechanism for conveyor belt (1) New hinge and belt 10

11 Wear mechanism for conveyor belt (2) Worn and damaged hinges and belt 11

12 Wear mechanism for conveyor belt (3) Experimental tests reveal a characteristic curve for belt elongation Detection of the belt elongation and thereby wear condition enables prediction of remaining useful life Elongation in mm Wear rate in % Solution: Monitor the belt elongation by measuring the distance between the metal bars by means of an AMR* smart sensor from Sensitec Running-in phase Linear belt elongation Failure phase *Anisotropic Magnetoresistive 12

13 Development steps for a smart sensor Physical sensor Smart sensor Smart signal processing Communication system Step 1: Identification & Analysis Step 2: Development of a physical sensor Step 3: Development of a smart signal processing Step 4: Development of a communication system Use-case: Potato harvester

14 AMR Sensor for monitoring belt condition Magnetoresistive field sensor with bias magnet to temporarily magnetise the ferromagnetic steel bars and hinges Large air gap between sensor and target (5-15 mm) Local signal conditioning (amplification) AMR Sensor Ferrite Bias Magnet Sensor signal in V Distance to sensor In mm Vertical distance from bar to sensor in mm Horizontal distance from bar to sensor in mm Sensor signal as a function of bar position Sensor signal in V 14

15 Preliminary laboratory tests Section of Conveyor Belt Prototype Sensor Element 15

16 Field tests Drive gear Conveyor Belt Sensor Steel bar 16

17 It works 17

18 Verification in a virtual development environment 18

19 Use case: Knife sharpness monitoring in a tobacco cutter Knife Sensor Bias magnet Sensor concept: Field sensor with bias magnet Tobacco cutting machine (Source: Hauni Primary GmbH) Objective: Real-time monitoring of knife sharpness in a tobacco cutter Monitoring of 8 knives on a drum rotating at 660 rpm Magnetic field simulation 19

20 Sensor principle of operation Knife Knife Knife Sensor As the knife passes by the sensor, the magnetic field (generated by the bias magnet) is deflected leading to an output signal from the AMR field sensor Typical signal form (*) Sensor output signal voltage (V) (*) The actual signal form depends on the overall geometry and on the material characteristics of the soft magnetic target Time (s)

21 Practical implementation (1) Sensor Knife edge Direction of rotation of knife drum 21

22 Practical implementation (2) Two sensors are applied: One sensor tracks along the edge of the knife. A second, non-moving reference sensor monitors a sharp region of the knife When the knife gets blunt the signal from one sensor is delayed relative to the signal from the reference sensor ( t) The sharpness of the knife can be judged from this temporal delay A linear drive is used to move one sensor along the edge of the knife A 2D-sharpness profile is generated for each knife, which is used to control the re-grinding process 22

23 Signal processing Sensor integration in machine Visualisation on machine HMI Customer benefit: Condition-based grinding rather than fixed re-grinding intervals with associated machine down time Machine builder benefit (added value): Securing business with original knives and original grinding materials 23

24 This works too. 24

25 Conclusions and Outlook The further growth of Product-Service Systems for capital goods depends strongly on the increased availability of field data via smart sensors Robust, precise, miniaturized and fast magnetoresistive (xmr) sensors provide a versatile technology for condition monitoring sensors For xmr sensors there is no compromise between accuracy, bandwidth and energy efficiency xmr sensors thereby provide a basis for intelligent wireless sensor networks, even under very demanding operating conditions The results from state-funded R&D projects and pilot customers are expanding the field of applications rapidly A virtual development environment optimizes the choice of sensor, sensor placement and signal processing in early development phases There are often surprising additional benefits from product-service systems 25

26 Thank you for your attention 2