EP2323105B1 - Überwachung von Maschinen - Google Patents

Überwachung von Maschinen Download PDF

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EP2323105B1
EP2323105B1 EP20090290794 EP09290794A EP2323105B1 EP 2323105 B1 EP2323105 B1 EP 2323105B1 EP 20090290794 EP20090290794 EP 20090290794 EP 09290794 A EP09290794 A EP 09290794A EP 2323105 B1 EP2323105 B1 EP 2323105B1
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modes
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machine
confusion
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EP2323105A1 (de
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Patricia Scanlon
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Alcatel Lucent SAS
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    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C3/00—Registering or indicating the condition or the working of machines or other apparatus, other than vehicles
    • G07C3/08—Registering or indicating the production of the machine either with or without registering working or idle time

Definitions

  • This invention relates to monitoring of machines.
  • it relates to monitoring of machinery performance, especially rotating machines and to predict failure time.
  • Rotating machinery such as fans, is often mission critical and it can be very important to be able to predict the lifetime and probable time-to-failure of such machinery in order that planned maintenance and replacement may take place with minimal downtime.
  • failure times are predicted by measuring at least one parameter of a machine in order to predict a time at which failure of the machine is anticipated based on said method parameter.
  • the parameters measured can be acquired from a number of different types of data such as airborne acoustic noise or structure borne acoustic emissions (ie vibrations).
  • airborne acoustic noise or structure borne acoustic emissions ie vibrations
  • the parameters acquired from these different types of data can be negatively affected by the presence of external noise.
  • This external noise may be background acoustic noise, external vibrations or noise from nearby machinery or vehicles or other externally generated noise. It might be acoustic noise or could also mean electromagnetic noise. Such external noise can lead to confusion in the system and poor results, leading to poor system performance.
  • US 6,490,543 B1 and US 2003/101019 A1 disclose a method of machine monitoring comprising measuring, over time, at least two different modes of data relating to the machine to obtain independent classifiers which are combined using decision fusion in a manner which takes into account environmental factors.
  • the present invention arose in an attempt to provide an improved method and system for predicting failure times of rotating machinery.
  • a method of machine monitoring comprising measuring, over time, at least two different modes of data relating to the machine to obtain independent classifiers which are combined using decision fusion in a manner which takes into account environmental factors, in order to provide an indication of wear of the machine; characterised in that the decision fusion takes into account confusion of output probabilities of each classifier, such that a relatively high confusion is represented by a relatively low signal to noise ratio of the monitored data for a mode of data and a relatively low confusion is represented by a relatively high signal to noise ratio of the monitored data for a mode of data.
  • the method is used for predicting time to failure (or remaining useful lifetime (RUL)).
  • the modes of data may be selected from various modes, such as acoustic noise, structure-borne noise (vibration) or temperature, for example.
  • a decision level adaptive fusion approach of multiple modes of data is used in order to predict failure time of the machine. This applies weightings to the fusion which takes into account the different effects that environmental factors, such as external noise, have upon the different modes.
  • the fusion method takes into account confusion of output probabilities of each classifier, whereby a relatively high confusion is likely to represent a relatively low signal to noise ratio of the monitored data for a mode and a relatively low confusion likely to represent a relatively high signal to noise ratio of the monitored data for a mode.
  • a fusion approach is known from 'Adaptive fusion of acoustic and visual sources for automatic speech recognition' Speech Communication 26 (1-2). 149-161, 1998 . In this example, it is used for automatic speech recognition. In some embodiments of the present invention, techniques of this type are used in a very different environment, to predict failure time of rotating or other machinery.
  • apparatus for measuring one or more parameters relating to functioning of a machine comprising means for monitoring, over time, two or more modes of data independently to obtain individual and independent classifiers and means for combining the result, using a decision fusion technique with a weighting according to the effect environmental noise has on each mode of data to determine said parameter, characterised in that the decision fusion takes into account confusion of output probabilities of each classifier, such that a relatively high confusion is represented by a relatively low signal to noise ratio of the monitored data for a mode of data and a relatively low confusion is represented by a relatively high signal to noise ratio of the monitored data for a mode of data.
  • the individual parameters measured may be used to obtain individual and independent classifiers, which can then be combined using decision fusion techniques in order to make a determination as to RUL or other parameters relating to functioning of a machine.
  • a measure of confusion may be used to determine how each different modality (parameter) is being effected by external noise factors. For example, measures of the entropy or variance of classifier output scores can be used to determine confusion levels of each mode.
  • weighting is used to weight the contribution of each mode before decision level multi-modal fusion, whereby modes that are relatively noise free are weighted more heavily than those modes that are more effected by external noise factors.
  • a machine 1 may comprise a rotating machine such as a fan, or possibly a plurality of machines.
  • At least two modes of data are measured. These may relate to, for example, acoustic airborne noise, structure borne noise (ie vibration), temperature (either at a point of the machine itself or in its vicinity
  • Figure 1 shows two such modes, in this case this might be acoustic data (airborne noise) 2 and vibration data 3 which measures variation at the machine.
  • any number of different modes of data may be measured.
  • Figure 2 shows an alternative arrangement comprising sensors for measuring. acoustic noise 2, vibrations 3 and temperature 4.
  • Sensors 2 to 4 in the figure may be any convenient type of sensor for measuring the relevant mode of data.
  • microphones or other transducers may be used to measure acoustic signal; temperature sensors to measure temperature; or other sensors may be used to measure other parameters.
  • Outputs from these are then applied to a computational unit 5 for further processing.
  • this may of course comprise a number of different units, such as individual units for extracting relevant features and classifiers from each of the modes of data and then a further unit for combining these, or this may all be done in a single unit.
  • Data is obtained from the various sensors at fixed or variable sampling rates over time while the machine is operating, or alternatively during an initial testing phase.
  • the various modalities ie acoustic input, vibration input, temperature input, and so on
  • the various modalities are processed independently, to generate respective independent classifiers.
  • Figure 3 shows a typical method for determining remaining useful lifetime (RUL) of a rotating machine.
  • two sets of data are obtained in this example. As described, however, more than two sets may be obtained, ie three or more types of data may be obtained.
  • the sets of data are acoustic data and vibration data.
  • the acoustic data is obtained from a suitable sensor 2 (eg a microphone) as raw acoustic samples 5. These samples may be obtained at periodic intervals, such as every second, every minute, every hour or any other time interval, which interval may vary. The samples will of course obtain, in addition to acoustic noise generated by the machine itself, environmental and external noise generated by external sources such as motor vehicles, external machinery and others.
  • vibration data is obtained from vibration sensor 3 and used to obtain raw vibration sample 6, again at the same or different sampling rates.
  • the vibrations from the machine will also be affected to external perturbations affecting the vibration of the machine and these might arise for example from air currents, the operation of other machinery or plant in the vicinity, movements of a vehicle in which the machine is in, or many other external factors which will affect the vibrations of the machine.
  • Each of the samples 5 and 6 is applied to a separate and independent feature extraction engine 7, 8 respectively.
  • Feature extraction is in itself an known technique and a set of representative features is obtained from each of the independent feature extraction engines 7 and 8. These are applied to respective classifiers for the acoustic data 9 and for the vibration data 10.
  • Classifiers are also well-known in themselves.
  • a classifier essentially represents a mapping from a discrete or continuous feature space to a discrete set of labels.
  • the classifier may be, for example, an indication of wear, such that classification A means that the machine is barely worn, classification B means that the machine is beginning to wear and classification C means that the machine is nearly worn out.
  • classification A may mean that the machine is zero to one third worn, classification B from one third to two thirds and classification C greater than two thirds worn, on a simplistic level.
  • the outputs from the independent classifiers 9 and 10 are then combined in a decision fusion technique at a decision level fusion engine 11 which combines them in order to determine a hopefully more accurate remaining useful life (RUL) prediction 12.
  • RUL remaining useful life
  • the decision fusion may be a multiplicative, additive or other process, including combinations of processes.
  • the output of the two classifiers 9 and 10 represent a list of all possible classes/outcome, ie worn, partially worn, not worn, and their associated scores (probability).
  • the scores are simply multiplied in the decision level fusion 11 or their log scores are added and the most likely output is determined.
  • a mechanism may also be provided which provides information on the reliability of each modality (ie acoustic and vibration input in the example of Figure 3 ) and thereby weightings that should be applied to each. That is, if it is found that acoustic data is more reliable in determining wear (or other parameters) than vibration data then greater emphasis and weighting may be placed upon the results from the acoustic classification than the vibration classification.
  • modality ie acoustic and vibration input in the example of Figure 3
  • weightings that should be applied to each. That is, if it is found that acoustic data is more reliable in determining wear (or other parameters) than vibration data then greater emphasis and weighting may be placed upon the results from the acoustic classification than the vibration classification.
  • Adaptive weightings are weightings that are dynamically adjusted during the fusion process.
  • the process is multiplicative using rules of probability.
  • Such a scheme initially selects a candidate that maximises the product of the N-best output probabilities of the modalities.
  • the N-best output probabilities of the acoustic and vibration mode are weighted according to the dispersion, variances or entropy of their N-best output probabilities. They will therefore account to some extent for the confusability of the N-best probabilities or outputs from each classifier which can be affected by external noise factors.
  • the weighting scheme accounts for the reliability of each of these modes.
  • each mode is then adaptively weighted before performing decision level multi-modal fusion.
  • each mode is evaluated and the mode that is considered to be least effected by external noise factors is weighted more heavily than that is more affected.
  • the two modes are acoustic and vibration data.
  • a separate classifier 9 and 10 respectively is trained for each type of data or mode.
  • For each sample presented to the classifier ie from the respective feature extraction module 7 and 8) a list of probabilities for each potential class is provided.
  • the classes may represent the degree of wear, or other parameters relating to the functioning and RUL of the machine.
  • the output classes for each class A to C given by the acoustic classifier 9 are:
  • the vibration classifier 10 is confused in that it provides broadly similar probabilities for all three classes. This could well be due to vibration data being corrupted with noise, which confuses the classifier. Alternatively, the vibration data may not provide enough information to clearly separate the classes A to C. Thus, a relatively high 'confusion' for a mode of data is considered to represent a low signal to noise ratio for that mode, suggesting the mode is more prone to noise interference. A low 'confusion' on the other hand indicates a higher signal to noise ratio, suggesting less noise interference.
  • the acoustic output can be weighted higher than the vibration output.
  • the acoustic output is considered to be much less affected by external environmental factors than the vibrational classifier.
  • the machine or machines upon which the process may be used might be a fan or other rotating machine operated in a factory, data centre or wind farm for example.
  • Embodiments may be used to provide indication of RUL of a machine (or group of machines) or other parameters or indications relating to wear, such as an indication of when a machine or a component is likely to need servicing or maintenance, or when it is likely to be X% worn, or other indications.

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
  • Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)

Claims (12)

  1. Verfahren zur Überwachung von Maschinen, umfassend das Messen, im Zeitablauf, von mindestens zwei unterschiedlichen die Maschine betreffenden Datenarten, um unabhängige Klassifikatoren zu erhalten, die unter Verwendung einer Entscheidungsfusion in einer Weise, welche Umgebungsfaktoren berücksichtigt, kombiniert werden, um eine Angabe über den Verschleiß der Maschine bereitzustellen; dadurch gekennzeichnet, dass die Entscheidungsfusion die Konfusion von Ausgabewahrscheinlichkeiten eines jeden Klassifikators berücksichtigt, wobei eine relativ hohe Konfusion ein relativ niedriges Signal-zu-Rausch-Verhältnis der überwachten Daten für eine Datenart anzeigt, und eine relativ niedrige Konfusion ein relativ hohes Signal-zu-Rausch-Verhältnis der überwachten Daten für eine Datenart anzeigt.
  2. Verfahren nach Anspruch 1, umfassend das Extrahieren eines oder mehrerer Parameter aus jeder Datenart, und das Verwenden eines jeden Parameters als Eingabe in einen jeweiligen Klassifikator für diese Art, wobei die Ausgaben der Klassifikatoren für das Ermitteln der sich auf den Verschleiß beziehenden Parameter verwendet werden.
  3. Verfahren nach Anspruch 1, eingesetzt für das Schätzen der verbleibenden nutzbaren Lebensdauer (RUL) der Maschine.
  4. Verfahren nach einem beliebigen der vorstehenden Ansprüche, wobei sich die Konfusion auf Bewertungswerte von Entropie oder Klassifikatorvarianten bezieht.
  5. Verfahren nach einem beliebigen der vorstehenden Ansprüche, wobei der Beitrag einer jeden gemessenen Datenart gewichtet wird und Arten, welche als relativ rauschfrei betrachtet werden, stärker gewichtet werden als diejenigen Arten, die mehr von externen Geräuschfaktoren betroffen sind.
  6. Verfahren nach einem beliebigen der vorstehenden Ansprüche, wobei ein Kandidat, welcher das Produkt der N-besten Ausgabewahrscheinlichkeiten der Datenarten maximiert, ausgewählt wird.
  7. Verfahren nach Anspruch 6, wobei die gemeinsame Wahrscheinlichkeit Pav der Datenarten einer jeden Klasse a, bei einer gegebenen Merkmalsmenge x, gemäß P AV k / χ = max i ⁢ P v ⁢ l i / χ λ x P A l i / χ 1 - λ
    Figure imgb0005
     ermittelt wird, wobei λ = σ v σ v + σ a ;
    Figure imgb0006
    Pav die gemeinsame Wahrscheinlichkeit der Schall- und Schwingungsmoden einer jeden Klasse k, bei einer gegebenen Merkmalsmenge x, darstellt, l die Anzahl möglicher Klassen darstellt, k eine der l Klassen darstellt; x die Merkmalsmenge darstellt; akustisch oder schwingend; σa und σv die Abweichungen der jeweils N-besten Ausgabewahrscheinlichkeiten der Audio- und Schwingungsmodalitäten darstellen.
  8. Verfahren nach einem beliebigen der vorstehenden Ansprüche, wobei die zwei oder mehr Datenmodalitäten für die Erzeugung jeweiliger Klassifikatoren verwendet werden und die Klassifikatoren für die Ermittlung der relativen Auswirkungen von Umgebungsgeräuschen auf den Messwert einer jeden jeweiligen Datenart verwendet werden, und wobei die Gewichtungen dementsprechend angewendet werden.
  9. Vorrichtung zur Messung eines oder mehrerer Parameter bezüglich der Funktionsfähigkeit einer Maschine, umfassend Mittel zur unabhängigen Überwachung, im Zeitablauf, von zwei oder mehreren Datenarten, um einzelne und unabhängige Klassifikatoren zu erhalten, sowie Mittel zum Kombinieren der erhaltenen einzelnen und unabhängigen Klassifikatoren unter Anwendung einer Entscheidungsfusionstechnik mit einer Gewichtung gemäß der Auswirkung der Umgebungsgeräusche auf jede Datenart, um den besagten Parameter zu bestimmen, dadurch gekennzeichnet, dass die Entscheidungsfunktion die Konfusion von Ausgabewahrscheinlichkeiten eines jeden Klassifikators berücksichtigt, wobei eine relativ hohe Konfusion ein relativ niedriges Signal-zu-Rausch-Verhältnis der überwachten Daten für eine Datenart anzeigt, und eine relativ niedrige Konfusion ein relativ hohes Signal-zu-Rausch-Verhältnis der überwachten Daten für eine Datenart anzeigt.
  10. Vorrichtung nach Anspruch 9, wobei der Parameter eine verbleibende nutzbare Lebensdauer (RUL) ist.
  11. Vorrichtung nach Anspruch 9 oder Anspruch 10, umfassend Sensoren zum Erfassen von mindestens zwei Datenarten, sowie rechentechnische Mittel, welche für den Empfang der Eingaben von den Sensoren und die Ermittlung der RUL ausgelegt sind.
  12. Vorrichtung nach Anspruch 11, wobei die Sensoren jeweils zur Messung der Geräusch- und Schwingungs-Datenarten vorgesehen sind.
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RU2476935C1 (ru) * 2011-08-26 2013-02-27 Учреждение Российской академии наук Санкт-Петербургский институт информатики и автоматизации РАН (СПИИРАН) Устройство для определения значений эксплуатационных характеристик изделия
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US6490543B1 (en) * 1999-07-13 2002-12-03 Scientific Monitoring Inc Lifeometer for measuring and displaying life systems/parts
DE10007308A1 (de) 2000-02-17 2001-08-23 Bosch Gmbh Robert Verfahren und Vorrichtung zur Ermittlung der verbleibenden Betriebsdauer eines Produktes
US6490545B1 (en) 2000-03-06 2002-12-03 Sony Corporation Method and apparatus for adaptive co-verification of software and hardware designs
GB2430039B (en) * 2005-09-07 2008-06-04 Motorola Inc Product age monitoring device and method of use of the device
EP1906273A1 (de) * 2006-09-29 2008-04-02 Siemens Aktiengesellschaft Verfahren zum Betreiben einer grosstechnischen Anlage sowie Leitsystem für eine grosstechnische Anlage
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