METHOD FOR ANALYZING CONDITIONS OF TECHNICAL COMPONENTS

Abstract

A method analyzes conditions of technical components in view of a rarity and/or an abnormality of a condition. To provide a reliable analysis and thus a safely operating system the method includes: a) describing conditions of the technical components in a behavioral input space that is spanned by state variables, which are characteristic for the technical components, b) analyzing a condition of one technical component in respect to other conditions of this technical component in the behavioral input space, whereby a rarity of this condition of the technical component is detectable, and c) analyzing the condition of the technical component also in respect to analyses of conditions of further technical components in the behavioral input space. Whereby an abnormality of the condition of the technical component is detectable.

Claims

1-14. (canceled)

15. A method for analyzing conditions of technical components in view of a rarity and/or an abnormality of a condition, the method comprises the following steps of: a) describing the conditions of the technical components in a behavioral input space being spanned by state variables, which are characteristic for the technical components; b) analyzing the condition of a technical component of the technical components in respect to other conditions of the technical component in the behavioral input space, whereby the rarity of the condition of the technical component is detectable, wherein step b) comprises the sub-steps of: determining a distribution of the conditions of the technical component in the behavioral input space for the analyzing of the conditions of the technical component; identify characteristic regions in the behavioral input space by using a distribution of the technical component in the behavioral input space; and determining a number of the conditions of the technical component in at least one characteristic region of the behavioral input space; c) analyzing the condition of the technical component also in respect to analyses of conditions of further technical components in the behavioral input space, whereby the abnormality of the condition of the technical component is detectable.

16. The method according to claim 15, wherein step a) of the method comprises the sub-step of generating the behavioral input space by using a statistic done on historical data of a behavior of the technical components.

17. The method according to claim 16, wherein step a) of the method comprises the sub-step of consolidating statistics for the generating of the behavioral input space of the conditions of the technical components.

18. The method according to claim 15, wherein each said condition of the technical component and of the further technical components in the behavioral input space is represented by a data point, wherein each said data point is characterized by a) its position, and b) a value indicating an originating component.

19. The method according to claim 16, wherein step a) of the method comprises the sub-step of obtaining the statistic by a method selected from the group consisting of: rescaling input signals, dimensionality reduction techniques and using derivatives gained by applying statistical metrics or transformations to input signals.

20. The method according to claim 15, wherein step c) of the method comprises the sub-step of determining a number of the conditions of the further technical components in the at least one characteristic region of the behavioral input space for analyzing the condition of the technical component also in respect to analyses of the conditions of the further technical components.

21. The method according to claim 15, wherein step c) of the method comprises the sub-step of obtaining the distribution of the conditions (10, 10′; 12′) in the behavioral input space by a method selected from the group consisting of: a simple density approach, statistical outlier selection, a machine learning based approach, component inference, an AI-based approach, and an approach based on a probability distribution comparison.

22. The method according to claim 15, wherein step c) of the method comprises the sub-step of determining a number of contributors for each characteristic region by a method selected from the group consisting of: counting of non-zero entries and In verse Participation Ratio.

23. The method according to claim 15, wherein in case of an evaluation of the condition of the technical component as unclassified in view of the rarity and/or the abnormality of the condition, the method further comprises the steps of: identifying the at least one characteristic region of the behavioral input space by checking if the unclassified condition fits into the at least one characteristic region; assuming the rarity of the unclassified condition if a number of classified conditions in the at least one characteristic region is lower than a first predefined threshold of a number of classified conditions contributing to the at least one characteristic region; and assuming the abnormality of the unclassified condition if a number of the classified conditions in the at least one characteristic region is lower than a second predefined threshold of a number of the classified conditions contributing to the at least one characteristic region; and classifying a before unclassified condition as a rare and abnormal classified condition in case of an assumption of the rarity and the abnormality.

24. The method according to claim 23, which further comprises the step of assuming a failure of the component in case of a classification of the before unclassified condition as the rare and abnormal classified condition.

25. The method according to claim 15, wherein the state variable of the conditions of the technical components includes at least one sensor value.

26. The method according to claim 15, wherein the technical component and the further technical components are components of a same type and/or the technical component and/or the technical further components is/are a train component.

27. The method according to claim 26, which further comprises selecting the train component from the group consisting of a motor, an air condition, an axle, a wagon, a carriage, a bogie, a wheel, a brake shoe, a brake pad, a spring, a screw, a bearing, a pantograph, a compressor, a transformer, other electrical systems, a coolant system, a fan motor, a computing system, a gearbox, a lighting system, a passenger door, an internal door, a lever, a microphone, an HVAC and an individual sensor.

28. The method according to claim 18, wherein each said data point is characterized by c) a time stamp or an interval of measurement.

29. A method for observation of a state of a technical component by analyzing conditions of technical components in view of a rarity and/or an abnormality of a condition, the method comprises the following steps of: a) describing the conditions of the technical components in a behavioral input space being spanned by state variables, which are characteristic for the technical components; b) analyzing the condition of a technical component of the technical components in respect to other conditions of the technical component in the behavioral input space, whereby the rarity of the condition of the technical component is detectable, wherein step b) comprises the sub-steps of: determining a distribution of the conditions of the technical component in the behavioral input space for the analyzing of the conditions of the technical component; identify characteristic regions in the behavioral input space by using a distribution of the technical component in the behavioral input space; and determining a number of the conditions of the technical component in at least one characteristic region of the behavioral input space; c) analyzing the condition of the technical component also in respect to analyses of conditions of further technical components in the behavioral input space, whereby an abnormality of the condition of the technical component is detectable; d) obtaining different chronological conditions of the technical component by monitoring a state of the technical component over a period of time; and e) assigning the rarity and the abnormality for each chronological condition.

30. A method for failure prediction of a technical component by analyzing conditions of technical components in view of a rarity and/or an abnormality of a condition, the method comprises the following steps of: a) describing the conditions of the technical components in a behavioral input space being spanned by state variables, which are characteristic for the technical components; b) analyzing the condition of a technical component of the technical components in respect to other conditions of the technical component in the behavioral input space, whereby the rarity of the condition of the technical component is detectable, wherein step b) comprises the sub-steps of: determining a distribution of the conditions of the technical component in the behavioral input space for the analyzing of the conditions of the technical component; identify characteristic regions in the behavioral input space by using a distribution of the technical component in the behavioral input space; and determining a number of the conditions of the technical component in at least one characteristic region of the behavioral input space; c) analyzing the condition of the technical component also in respect to analyses of conditions of further technical components in the behavioral input space, whereby an abnormality of the condition of the technical component is detectable; d) assuming a failure of the technical component in dependency of a classification of the condition of the technical component as rare and abnormal.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0067] The present invention will be described with reference to drawings in which:

[0068] FIG. 1: shows schematically a train with several technical components and an analysis system for analysing of conditions of the components in view of a rarity and/or an abnormality,

[0069] FIG. 2: shows a block-diagram of an operational strategy of the analysis method,

[0070] FIG. 3: shows in a diagram the density distributions of four different components and

[0071] FIG. 4: shows in a diagram the color-coded distribution of the noteworthy-ness of the operation states of one component from FIG. 3.

DETAILED DESCRIPTION OF THE ILLUSTRATED EMBODIMENTS

[0072] FIG. 1 shows in a schematically view a pre-determined track 28 of a railway system 30, like, for example, the German or Russian mainline railway or Munich subway. Moreover, FIG. 1 shows a mobile unit, like a track-bound vehicle, e.g. a train 32 in the form of a high speed train 32, being moveable on the pre-determined track 28.

[0073] The railway system 30 further has a control centre 34 that comprises a computer 36 equipped with an appropriate computer program that comprises instructions which, when executed by the computer 36, cause the computer 36 to carry out the steps of an analysis method. Alternatively, the computer 36 may be located on board of the train 32. The proposed method can be used for predicting a failure F of a component 14 or a train component 24, respectively, like a motor 26 of a wagon, of the train 32 (details see below).

[0074] Normally, conditions 10 of several components 14, 14′, 16 can be analysed simultaneously. In this specification one condition 10 of one component 14 alone will be examined or explained exemplarily as an active component 14 in the analysing process and the failure prediction. The further components 14′, 16 will each be viewed as a passive element. However, since normally the condition 10 of several components 14, 14′, 16 might be changing the analysis may be done for each component 14, 14′, 16 individually.

[0075] Moreover, the control centre 34 comprises as part of the computer 36 an analysis system 38 comprising a receiving device 40 to receive as input data sensor values S of the condition 10 of the component 14. Moreover, the analysis system 38 comprises a storage device 42 for storage of parameters, like historic data D (as sensor values S with relating time points t1, t2) or predefined first and second threshold H, h (boundary value or limit) with numbers Q, q of conditions 10′, 12′ needed to be not exceeded to meet the threshold H, h. Further, the analysis system 38 comprises an evaluating device 44 to process or evaluate the conditions 10, 10′, 12′ of the components 14, 14′, 16 in view of rarity R, r and/or abnormality Y, y of the conditions 10, 10′, 12′. The receiving device 40 and the evaluating device 44 are processing devices.

[0076] The control centre 34 may be supervised by an operator 46 which may also receive issued outputs, like information concerning rarity R, r or abnormality Y, y or a failure F as result of the failure prediction or a time point (time stamp TS) for a replacement of a component (details see below). The operator 46 may also be a driver of the train 32 or on-board of the train 32.

[0077] As stated above, the invention concerns a method for analysing of conditions 10, 10′, 12′ of technical components 14, 14′, 16 in view of a rarity R, r and/or an abnormality Y, y of a condition 10, 10′, 12′. Condition 10 is the actual state of the component 14, like the motor 26 of one wagon, of the train 32. Conditions 10′ and 12′ are historical data D of the component 14 (condition 10′) and of the further components 14′, 16 (condition 12′). Therefore, the train 32 from which the historical data D were obtained is shown in broken lines. The conditions 10, 10′, 12′ are represented by state variables V that comprises at least one sensor value S or are sensor values S, like a temperature or a pressure. The component 14 and the further component 14′ are components 14, 14′ of the same type. In other words, both are motors 26 of different wagons of the train 32. The components 14, 16 may also be of a different kind. However, in that case their state variables V need to have a known correlation towards each other.

[0078] In the following description only components 14, 14′ and the conditions 10, 10′, 12′ will be described.

[0079] The analysing method will now be described in reference to FIG. 1 and FIG. 2, wherein the latter shows a block-diagram of the operational strategy of the analysing method.

[0080] In a first step or in step A of the method the conditions 10, 10′, 12′ of the technical components 14, 14′ are described in a conditional/behavioural input space 20 that is spanned by the state variables V, which are characteristic for the technical components 14, 14′.

[0081] The first step of the normal behaviour finding can be visualized best by considering each input measure (normally a specific sensor value, operational state or derivative of those) as one dimension of a large behavioural input space 20. Hence, each data point P in the time-series of these measures is one point in this input space 20. Combining all data points P of all components 14, 14′, a density distribution in the input space 20 can be obtained, where each condition 10, 10′, 12′ of said technical component 14 and of the further technical components 14′ in the behavioural input space 20 is represented by a data point P. Each data point P is characterized by a) its position=input values or derivatives and b) a value indicating the originating component 14, 14′ and c) the time stamp TS or interval of measurement.

[0082] The behavioural input space 20 can be generated by using a statistic done on the historical data D of the behaviour of the technical components 14, 14′. In practice, there are various methods possible to achieve the above embedding of the state variables V or the input values into a suitable behavioural input space 20. Most notably, one can use suitable positions by rescaling input signals, dimensionality reduction techniques (e.g. PCA) or using other derivatives. Also, the embedding does not need to be continuous, but one may also have a categorical axis, such as predictions made by a classifier applied to the original data.

[0083] In summary, first the state variables V or the input data are embedded into the suitable input space 20, in which a position indicates a combination of sensor values S or characteristics for a given component 14, 14′. Doing this for all components 14, 14′ individually, obtain a set of multi-variate distributions in this space 20, one for each component 14, 14′

[0084] An example for the input space 20 that can be analysed is shown in FIG. 3. More specifically, it shows two input metrics on the X and Y axis, each data point P indicating one observed combination. The symbols (black cycle, open cycle, open triangle, cross) indicate the component 14, 14′ assigned to each data point P (indicated with reference numerals for two components 14 (black cycle), 14′ (open cycle) only).

[0085] When the “input” for four different components 14, 14′ is overlaid, it can be observed that the distribution of their data points P is different in some regions and identical in others.

[0086] In the second or in step B) of the method a condition 10 of the technical component 14 is analysed in respect to other conditions 10′ of this technical component 14 in said behavioural input space 20, whereby a rarity R of this condition 10 of said technical component 14 is detectable.

[0087] In this further step the statistics are consolidated. For the analysing of the conditions 10 of said technical component 14 the distribution of the conditions 10, 10′ of said technical component 14 in the behavioural input space 20 is determined. In other words, the different component's 14, 14′ distributions in the input space 20 are consolidated, so that they can be compared with each other. For the comparison, the raw data points P for different regions 18, 18′ of the input space 20 must be aggregated in such a way that comparable metrics for each region 18, 18′ and for each distribution will be obtained. More specifically, for each region 18, 18′ a vector containing as entries a metric characterizing how much each component 14, 14′ contributes to the data points P in that region 18, 18′ will be established.

[0088] There are various ways to obtain these vectors that indicate component contribution in different regions 18, 18′ of the input space 20. They range from simply computing the relative density of data points P from each component 14, 14′ in a cube of the input space 20 to using neuronal networks for inferring the probability of a point P in a region 18, 18′ origination from a given train 32, using clustering to identify the most significant portions of the input-space 20 or rare events only. These methods for consolidating the raw input data into comparable aggregated distributions for each component 14, 14′ are detailed in the following passage.

[0089] The target of the three presented methods is to aggregate a set of raw data into an aggregated “region-centered” per-component distribution in the input space 20. In other words, the vector V_regionindex, containing as entries the per-component contributions of the input data in different regions 18, 18′ of the input space 20 should be established. The methods are exemplary explained with trains as components 14, 14′ and without reference numerals for better readability.

[0090] Approach 1—Simple Density

[0091] In this approach density measurement technique is used to get a probability mapping region to the set of trains. The method has multiple steps which are described below. [0092] Initially a scatter plot of sensor signals or values is formed and it is divided into N power (No of senor signals) regions, where N=(1, 2, 3, . . . N). The plot is divided into a suitable number N of individual regions, such as multidimensional cubes that fill in the whole state space. For instance, if two dimensions are used as in the examples, then the input space is divided into rectangles (=cubes of dimension 2). [0093] Each region in the scatter plot will have samples from different trains. Some regions may be populated with samples from all the trains, some regions from few trains, some from single train and some regions might be empty. [0094] A multi-label vector Y_regionindex=[y_k], k=(1, 2, 3 . . . M) denotes the train number, M is the no of trains, is assigned to each divided region. y_k denotes the number of points from train k in that particular region. Here the density is calculated with the basic counting technique and it can be replaced with any sophisticated density calculation techniques. [0095] Possibly multiple smoothing or convolutional filters, interpolation or other splining techniques are applied to obtain a smooth and continuous sensor reading density distribution. [0096] The multi label vector is normalized to have a unit vector which in turns acts as a probability mapping of the region to the train. [0097] This normalized vector is passed through the generic mathematical model which is explained before to get the anomalous scores.

[0098] Approach: 2 (Machine Learning Based—Component Inference)

[0099] In this approach a supervised machine learning technique is used to get a probability mapping of each region in the space of sensor readings to the set of trains. The method has multiple steps which are described below. [0100] Initially a scatter plot of sensor signals is formed and it is divided into N power (No of senor signals) regions where N=(1, 2, 3, . . . N) [0101] Each region in the scatter plot will have samples from different trains. Some regions may be populated with samples from all the trains, some regions from few trains, some from single train and some regions might be empty. [0102] A multi-label vector Y_regionindex=[y_k], k=(1, 2, 3 . . . M) denotes the train number, M is the no of trains, is assigned to each divided region. y_k=1 if the train k have points in the given region and y_k=0 if the train k does not have any points in the given region. i.e., Y_regionindex=[1, 0, 1, 1] in the given example, we see that the given region index is populated with the points from trains 1, 3, 4 but not from 2. [0103] A supervised machine learning algorithm, in our case convolutional neural network is chosen to learn the mapping from the input regions to the output multi-label array assignment. The input regions and the corresponding multi-label vector act as training samples for our neural network training. The model learns the function F which maps the region to multi-label vector assignment [0104] Once the model is trained during operation time each region is passed through the model and the multi-label vector is predicted with the model. The predicted vector is normalized to make a probability mapping of the region to the train. [0105] This predicted vector is passed through the generic mathematical model which is explained before to get the anomalous scores.

[0106] Training:

[0107] Input sensors->(Regions, multi-label vector)->F->F_learned model

[0108] Operation:

[0109] Input sensors->(Regions)->F_learned model->multi-label vector->Normalization->multi_lable norm_vector->Mathematical model based Anomaly scorer->Anomalous scores

[0110] Approach: 3 (Probability Distribution Comparison Based)

[0111] In this approach Earth mover's distance (EMD) is used to get a probability mapping of region to the set of trains. The method has multiple steps which are described below. [0112] Initially a scatter plot of sensor signals is formed and it is divided into N power (No of senor signals) regions where N=(1, 2, 3, . . . N) [0113] Each region in the scatter plot will have samples from different trains. Some regions may be populated with samples from all the trains, some regions from few trains, some from single train and some regions might be empty. Multidimensional normalized histogram (proxy of probability distribution) for each train in a region is formulated of each of the regions. [0114] In each region the similarity between one histogram (one train) with the other histogram (other trains) is calculated using Earth Mover's distance. Each train will have a list of similarity scores S_k=(sk1, sk2, . . . skM), where k=(1, 2, 3, . . . M) denotes the train number, where m is the number of trains. [0115] A multi-label vector Y_regionindex=[y_k], k=(1, 2, 3 . . . M), denotes the
train number M is the no of trains, is calculated to each divided region. y_k is the average of all the scores in S_k. [0116] The multi label vector is normalized to have a unit vector which in turns acts as a probability mapping of the region to the train. [0117] This normalized vector is passed through the generic mathematical model which is explained before to get the anomalous scores.

[0118] Here, the computation of the component-contribution vectors through a simple approach will be exemplarily illustrated. The input space 20 is sliced into cubes of equal size and the density of points P inside each cube is computed for each component 14, 14′. For the example in FIG. 3, the input space is divided into small squares and the number of points P inside each square relative to the total number of points P for the component 14, 14′ is computed (not shown). In other words, for each square (“region”) a vector with the entries v_i=N_i(region)/N_i(total) is established, wherein i indicates the different components 14, 14′.

[0119] Hence, characteristic regions 18, 18′ in the behavioural input space 20 are identified by using the distribution of said component 14 in the behavioural input space 20. Then a number U of conditions 10, 10′ of said technical component 14 in at least one characteristic region 18, 18′ of the behavioural input space 20 is determined.

[0120] According to a third step or step C) of the method said condition 10 of said technical component 14 is also analysed in respect to analyses of conditions 12′ of further technical components 14′ in said behavioural input space 20, whereby an abnormality Y of said condition 10 of said technical component 14 is detectable. Thus, a number u of conditions 12′ of the further technical components 14′ in said at least one characteristic region 18, 18′ of the behavioural input space 20 is determined for analysing said condition 10 of said technical component 14 also in respect to analyses of conditions 12′ of further technical components 14′.

[0121] Hence, the third step is to identify regions 18, 18′ of abnormal behaviour through the vectors v_i. Intuitively speaking, regions 18, 18′, where a) the number M of components 14, 14′ contributing is low and b) the characteristics of a component 14, 14′ is rare should be identified. For this, metrics that identify a) from the vector contributions are required. The simplest metric for this is counting non-zero entries, more advanced metrics are the Inverse Participation Ratio (IPR) (SUM(v_i{circumflex over ( )}4)/SUM(v_i){circumflex over ( )}2), which i ranges between 1/#Components and 1 depending on the number M of contributing components 14, 14′ or contributors 22, 22′. #Components=Number of components, i.e. when having 4 components 14, 14′ then the vector has 4 entries and the IPR>1/4. Moreover, “i” runs over the component entries 1 . . . 4.

[0122] Indicating the IPR for the above example results in the diagram shown in FIG. 4, which shows the abnormality scores extracted from the density distributions of FIG. 3: The grid placement of the points is due to the square regions that was used to aggregate, each point represents the value of a given region 18, 18′. The “degree of grey” indicates how “abnormal” that given regions 18, 18′ is according to the IPR, black indicates abnormal and white normal regions.

[0123] Using a component-aware distribution gives a much more detailed picture of normal and anomalous behaviour. For instance, the region of (0, 0) is flagged as normal N despite a very low number of data points P, because almost all components 14, 14′ show this behaviour sometimes. At the same time, the data point P at the right bottom is flagged as unusual or abnormal Y, because data points P in this region 18, 18′ are only exhibited by few components 14, 14′. While the black region in the middle would have been identified as normal N by any standard approach, this level of detail makes it possible to more granularly distinguish rare from abnormal behaviour.

[0124] Based on the multi-component distributions, now any new or existing data points P as normal N or anomalous Y can be identified. More specifically, for a given data point P, the position of the data point P in the input space 20 can be computed and from this how “abnormal” it is with regard to the distribution of its original component 14, how “rare” it is with regard to the joint distribution of all other components 14′, but also how “component-wise abnormal” it is with regard to each other component 14′. As shown in FIG. 3 data points P or conditions 10, 10′, 12′ clustered in the densely middle region 18′ will be assessed as often O (not-rare) and normal N (not abnormal) for the conditions 10, 10′ of the component 14 (black cycle) and as often o and normal n for the condition 12′ of the further component 14′ (open cycle). However, data points P or conditions 10, 10′, 12′ in a less populated region 18 (not marked by a square and with a reference number 18) will be assessed as rare R and abnormal Y for the conditions 10, 10′ of the component 14 (black cycle) and as rare r and abnormal y for the condition 12′ of the further component 14′ (open cycle)

[0125] Hence, in case of an evaluation of a condition 10 of a technical component 14 as unclassified in view of a rarity R and/or an abnormality Y of the condition 10, the method comprises the steps of: identifying a characteristic region 18, 18′ of the behavioural input space 20 by checking by the evaluation device 44 if the unclassified condition 10 fits into said characteristic region 18, 18′, assuming a rarity R of said unclassified condition 10 if a number U of classified conditions 10′ in the characteristic region 18, 18′ is lower than the first predefined threshold H of the number Q of classified conditions 10′, 12′ contributing to said characteristic region 18, 18′, and assuming an abnormality Y of said unclassified condition 10 if a number U, u (also the sum of the numbers U and u) of classified conditions 10′, 12′ in the characteristic region 18, 18′ is lower than the second predefined threshold h of the number q of classified conditions 10′, 12′ contributing to said characteristic region 18, 18′, and in case of the assumption of rarity R and abnormality Y classifying the before unclassified condition 10 as rare and abnormal classified condition 10.

[0126] For example, the first boundary value/threshold H is a number Q of a maximum of three conditions 10′ of component 14 and the second boundary value/threshold h is a number q of a maximum of ten conditions 10′ 12′ of at least three different components 14, 14′. It was identified that the unclassified condition 10 fits into region 18 (not shown in detail). In this region 18 the number U of conditions 10′ of component 14 contributing to this region 18 is two and the number U, u of conditions 10′, 12′ of components 14, 14′ contributing to this region 18 is nine conditions 10′, 12′ of four components 14, 14′ (the number U of two conditions 10′ of component 14, as numbers u the sum of three conditions 12′ of a first further component 14′ and two times two conditions 12′ of a second and third further components 14′). The value two is fitting the criteria of the number Q of the first boundary value H of “a maximum of three conditions 10′”. Further, the value nine is fitting the criteria of the number q of the second boundary value h of “a maximum of ten conditions 10′ 12′ of at least three different components 14, 14′”. Hence, the unclassified condition 10 would be assessed as being rare R and abnormal Y.

[0127] Moreover, in a further step of the inventive method a failure F of the component 10 is assumed in case of a classification of the before unclassified condition 10 as a rare and abnormal classified condition 10.

[0128] The computation of abnormality Y, rarity R and component-abnormality for each data point P allows for a detailed assessment of component health: First, we can use the time-development of a combined score of these three indicators to identify when a component 14 develops anomalous behaviour with regard to its own components history (e.g. through temporal autocorrelation with past measures). Second, running a clustering algorithm on the multi-component distribution that splits regions 18, 18′ with high component-abnormality score and low rarity, from regions 18, 18′ with high rarity and low component abnormality can automatically distinguish abnormal behaviour of one or multiple components 14 that is due to rare operation or systematic component abnormal behaviour. Hence, this detection of true abnormalities allows distinguishing if the component is needed to be maintained or not. Third, rarity and per component abnormality can be used on new data points P to classify them as normal or unusual/abnormal Y with respect to the fleet other components 14′, 16 and the own component 14 allowing to flexibly assess abnormality and therefore risk for failure F.

[0129] Hence, the method can be used for an observation of a state of the technical component 14, wherein the use comprises the steps of: obtaining different chronological conditions 10, 10′ of the technical component 14 by monitoring the state of the technical component 14 over a period of time t1, t2, and assigning rarity R and abnormality Y for each chronological condition 10, 10′. Through this a time point may be selected to indicate when this type of component 14 needs to be replaced. This time point is represented by the time stamp TS of condition 10.

[0130] Moreover, the method can be used for a failure prediction of the technical component 14, wherein the use comprises the step of: assuming a failure F of the technical component 14 in dependency of a classification of a condition 10 of the technical component 14 as rare R and abnormal Y.

[0131] It should be noted that the term “comprising” does not exclude other elements or steps and “a” or “an” does not exclude a plurality. Also elements described in association with different embodiments may be combined. It should also be noted that reference signs in the claims should not be construed as limiting the scope of the claims.

[0132] Although the invention is illustrated and described in detail by the preferred embodiments, the invention is not limited by the examples disclosed, and other variations can be derived therefrom by a person skilled in the art without departing from the scope of the invention.