PREDICTIVE MAINTENANCE METHOD AND SYSTEM

• US 20150227122A1
• Filed: 09/16/2013
• Published: 08/13/2015
• Est. Priority Date: 09/19/2012
• Status: Active Grant

• Current Assignee: Konecranes Global Corporation (Konecranes Terex Plc)
• Original Assignee: Konecranes PLC (Konecranes Terex Plc)
• Inventors: Martinkallio, Tuomas, Hrknen, Tuomo, Luotonen, Henri, Pahlman, Marika
• Granted Patent: US 9,760,068 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: B66C 13/18 Control systems or devices exclusively for rope, cable, or chain winding mechanisms B66D1/40
• CPC Class Codes: G05B 13/048 using a predictor
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: Y02P 90/80 Management or planning

Abstract

The invention relates to a predictive maintenance of hoisting equipment (102), particularly cranes. A maintenance centre (106) automatically collects diagnostic data relating to least one component of the remote hoisting equipment and optionally sensor data relating the operational environment of the remote hoisting equipment (102). The maintenance centre (106) has an access to configuration data of the remote hoisting equipment and the reliability data on the at least one component of the remote hoisting equipment (102). The maintenance centre (106) is then able to generate a maintenance plan optimizing the cost of maintenance and reliability of the hoisting equipment over a life cycle of the hoisting equipment.

Claims

1-14. -14. (canceled)

15. A predictive maintenance method of hoisting equipment, particularly cranes, comprising the steps of:

receiving and automatically collecting, at a maintenance centre, diagnostic data relating to at least one component of a remote hoisting equipment;

receiving and automatically collecting, at the maintenance centre, sensor data relating the operational environment of the remote hoisting equipment at least from a corrosion sensor and/or an accelerator sensor installed at the remote hoisting equipment, said data from the corrosion sensor comprising data representing a corrosive effect of the environment on electrical devices of the remote hoisting equipment and/or said data from the accelerator sensor comprising the data of external shocks to and collisions of the remote hoisting equipment;

providing configuration data of the remote hoisting equipment;

providing reliability data on said at least one component of the remote hoisting equipment; and

automatically generating, based on said diagnostic data, configuration data, reliability data and said operational environment data, a maintenance plan optimizing the cost of maintenance and reliability of the hoisting equipment over a life cycle of the hoisting equipment.

16. The method as claimed in claim 15, wherein the automatically generating comprises the steps of:

estimating a reliability and a maintenance cost of the remote hoisting equipment for a plurality of different combinations of maintenance actions and schedules; and

selecting, among the plurality of combinations, the combination of maintenance actions and schedules that most probably optimizes the cost of maintenance and reliability of the remote hoisting equipment over a life cycle of the remote hoisting equipment.

17. The method as claimed in claim 15, wherein the automatically generating comprises the steps of:

utilizing equipment-type-specific reliability data maintained based on historian data collected from a plurality of remote equipment; and

utilizing the collected information to optimize a local maintenance of individual equipment.

18. The method as claimed in claim 15, wherein the selecting comprises the step of selecting the combination of maintenance actions that is estimated provide the best compromise between the cost and reliability or safety.

19. The method as claimed in claim 15, further comprising the step of, after automatically generating the optimized maintenance plan, manually tuning the maintenance plan.

20. The method as claimed in claim 15, comprising the step of maintaining in a database a customer-specific data that include one or more of configuration data of the remote equipment, types of the remote hoisting equipment, types of components of the remote hoisting equipment, customer preferences, designed usage profiles.

21. The method as claimed in claim 15, comprising the step of maintaining in a database equipment-type specific data that include parameters, features and/or reliability data for each type of equipment and/or component.

22. The method as claimed in claim 15, wherein the collected diagnostic data include operational, usage or condition data from the remote hoisting equipment.

23. The method as claimed in claim 22, wherein the operational, usage or condition data from the remote hoisting equipment is one or more of:

hoist starts, hoist work cycles, hoist running hours, hoist loading data, hoist over-temperature occurrences, overloads, emergency stops, data on any occurrence where the performance limits of the remote hoisting equipment are exceeded.

24. The method as claimed in claim 15, wherein the sensor data relating to the operational environment of the remote hoisting equipment further include temperature and/or humidity.

25. The method as claimed in claim 15, wherein the accelerator sensor measures acceleration in three dimensions in x, y and z directions of a Cartesian coordinate axes.

26. The method as claimed in claim 15, comprising the step of taking the operational environment data into account in estimating the reliability of the remote hoisting equipment, at least if operation in an abnormal environment is observed.

27. A predictive maintenance system comprising at least one computer and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the at least computer, cause a predictive maintenance of hoisting equipment, particularly cranes, said predictive maintenance comprising:

receiving and automatically collecting, at a maintenance centre, diagnostic data relating to at least one component of a remote hoisting equipment;

receiving and automatically collecting, at the maintenance centre, sensor data relating the operational environment of the remote hoisting equipment at least from a corrosion sensor and/or an accelerator sensor installed at the remote hoisting equipment, said data from the corrosion sensor comprising data representing a corrosive effect of the environment on electrical devices of the remote hoisting equipment and/or said data from the accelerator sensor comprising the data of external shocks to and collisions of the remote hoisting equipment;

providing configuration data of the remote hoisting equipment;

providing reliability data on said at least one component of the remote hoisting equipment; and

automatically generating, based on said diagnostic data, configuration data, reliability data and said operational environment data, a maintenance plan optimizing the cost of maintenance and reliability of the hoisting equipment over a life cycle of the hoisting equipment.