Data processing: generic control systems or specific application – Specific application – apparatus or process – Product assembly or manufacturing
Reexamination Certificate
2002-07-29
2004-07-06
Von Buhr, Maria N. (Department: 2125)
Data processing: generic control systems or specific application
Specific application, apparatus or process
Product assembly or manufacturing
C700S029000, C706S912000, C714S047300
Reexamination Certificate
active
06760639
ABSTRACT:
BACKGROUND OF THE INVENTION
Field of the Invention
The invention relates to a system and a method for determining the effectiveness (overall equipment effectiveness (OEE)) of production installations, fault events and the causes of the fault events significantly contributing to losses in productivity.
Effectiveness is understood here as the concept of “Overall Equipment Effectiveness, OEE”, which is described for example in the reference by Robert Hansen, titled “Learning the Power of Overall Equipment Effectiveness”, and in the 1999 conference report Machinery Reliability Conference and Exposition, titled “The Meeting of Machinery Reliability Minds”, April 12-14, Cincinnati, Ohio, pages 19 to 30, published by Industrial Communications, Inc., 1704 by Natalie Nehs Dr., Knoxville, Tenn. 37931.
OEE is accordingly a method for determining a percentage that indicates to what extent the actual productivity in each case reaches a planned, that is prescribed, productivity. OEE is also referred to as the multiplication of synergistic parameters, which define the “health” of a process, to be specific OEE =availability ×processing speed ×quality.
For commercial reasons, and to safeguard product quality, operators of production installations have an interest in determining a desired effectiveness, which can be achieved in an undisturbed operation, and comparing the effectiveness at a given time with it. If the effectiveness at a given time deviates from the desired value, this indicates losses in productivity. It must then be determined which fault events exist and what is causing them. The causes may have their roots in physical, human or organizational areas.
Various methods and techniques can be used for the analysis of faults (in the sense of losses in productivity). The most important of these are failure modes and effects analysis (FMEA), fault tree analysis, or methods of statistical evaluation, such as for example the Pareto analysis [by John Moubray, RCM2, Butterworth-Heinemann, Second Edition 1997].
In the implementation of an FMEA, the following steps are taken:
a) functional breakdown of the installation;
b) description of main function and secondary function;
c) description and listing of functional fault statuses;
d) determination of all causes for each fault status;
e) determination of the effects on production objectives; and
f) quantitative assessment of the effects.
Fault tree analysis starts from an undesired TOP state. For this starting event, all the event situations that lead to this state are determined.
Statistical methods presuppose a suitable base of data from production. For example, with a Pareto analysis, those causes of faults that are responsible for the main production faults can be determined. FMEA and fault tree analysis can be supported by tools. Such tools guide the user step by step through the method, provide support in data acquisition and document the results.
The statistical methods presuppose, however, a suitable database, which is often not present. Either no data at all from production are recorded or else the information that would be necessary for a fault analysis is not acquired.
The methods mentioned above have their roots in engineering design, i.e. they are used for configuring a product or an installation to be as fail-safe as possible. The high standard of quality of the product reached justifies the considerable expenditure in terms of time and work for such analyses.
The ‘post-mortem’ analysis of losses and faults in a production installation is often time-critical, since the sustained loss in productivity entails considerable costs. A further disadvantage is that the methods do not support any procedure focusing on the cause of the fault at a given time.
It is known from the literature that there may be up to 7 cause levels between the fault events and the actual cause of the fault [John Moubray, RCM2, Butterworth-Heinemann, Second Edition 1997]. None of the known methods can indicate when the suitable level, which ensures lasting elimination of the cause of the fault, has been found.
SUMMARY OF THE INVENTION
It is accordingly an object of the invention to provide a system and a method for determining the effectiveness of production installations, fault events and the causes of faults which overcome the above-mentioned disadvantages of the prior art devices and methods of this general type, which make possible an automated determination of the effectiveness at a given time, fault events and the causes of faults.
With the foregoing and other objects in view there is provided, in accordance with the invention, a system for determining an effectiveness of production installations of various types, significant fault events which bring about deviations from a prescribed desired effectiveness, and causes of the significant fault events. The system contains a data acquisition device to be connected to a respective production installation and set up for continuous acquisition and ready-to-call-up storage of data including installation-related data and production-related data. A service device is connected to the data acquisition device. The service device includes an input device for inputting additional descriptive data including installation-related descriptive data and production-related descriptive data that cannot be called up from the data acquisition device, and a display device for displaying the effectiveness determined, the significant fault events and the causes of the significant fault events. An online system part is connected to and set up for calling up the installation-related data and the production-related data from the data acquisition device. The online system part has a fault event detector detecting the significant fault events on a basis of the data, the additional descriptive data input by the input device, and on an overall equipment effectiveness (OEE) script. The online system part determines the significant fault events by fault event evaluation using a configured assessment model, determines in each case the causes of the significant fault events using a configured fault model, and calculates the effectiveness. An offline system part is connected to the online system part. The offline system part contains or has access to a number of models including generic fault models and assessment models. The offline system part is set up for searching for the models on a basis of at least of called-up and/or input descriptive data. The offline system part configures the models and stores the models locally or in a locally distributed form. The offline system part is configured for storing the models in the online system part as the configured assessment model or the configured fault model.
The system according to the invention includes a service device, which is preferably configured as a mobile device and can be connected in each case to a data server in the master control system of a production installation. Both the method used and the implementation as a system are based on the use of pre-configured solution models. Such solution models can be established by an offline system part and be used in an online system part.
The service device can be used in an advantageous way for the analysis of causes in different production installations, for example both for the analysis of the causes of drops in productivity or inferior product quality in the making of paper and in filling installations for the filling of liquids in the food industry. This universal applicability is achieved by a series of generic models and by pre-configured assessment and fault models.
In accordance with an added feature of the invention, the respective production installation is a single machine or an installation having a number of machines.
In accordance with an additional feature of the invention, the data acquisition device is part of a master control system or a programmable controller.
In accordance with another feature of the invention, the service device is set up for using a web browser to access models
Greulich Manuel
Kallela Jari
Milanovic Raiko
Vollmar Gerhard
ABB Research Ltd.
Greenberg Laurence A.
Mayback Gregory L.
Stemer Werner H.
Von Buhr Maria N.
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