Cross-sectional performance monitoring of a measurement system

Abstract

A computer-implemented method for monitoring a performance of a measurement system configured to analyse a sample comprises the steps of i) Receiving a plurality of cross-sectional operating parameters being associated with an operation of a plurality of measurement systems configured to analyse a sample, and ii) Monitoring a performance of at least one particular measurement system based on an association of at least one operating parameter of said particular measurement system with the cross-sectional operating parameters of at least a subset of the measurement systems.

Description

[0001] TITLE

[0002] CROSS-SECTIONAL PERFORMANCE MONITORING OF A MEASUREMENT SYSTEM

[0003] TECHNICAL FIELD

[0004] The present invention relates to a computer-implemented method for monitoring a performance of a measurement system configured to analyse a sample according to claim 1 , and to a measurement system for analysing a sample comprising a processor configured to carry out said method according to claim 22.

[0005] PRIOR ART

[0006] Measurement systems configured to analyse a sample such as liquid chromatographymass spectrometer (LC-MS) are complex systems. Their performance is affected by several hundreds of operating parameters. If the performance is not optimal, troubleshooting is difficult and may take a long time. Moreover, early detection and elimination of an inferior performance is crucial for ensuring reliability of the sample analysis.

[0007] SUMMARY OF THE INVENTION

[0008] It is an object of the present invention to provide a method for monitoring a performance of a measurement system that is less complex and in particular requires no expert knowledge.

[0009] This object is achieved with a computer-implemented method according to claim 1. That is, a computer-implemented method for monitoring a performance of a measurement system configured to analyse a sample is provided, wherein the method comprises the steps of i) receiving a plurality of cross-sectional operating parameters being associated with an operation of a plurality of measurement systems configured to analyse a sample, and ii) monitoring a performance of at least one particular measurement system of said plurality of measurement systems based on an association of at least one operating parameter of said particular measurement system with the cross-sectional operating parameters of at least a subset of the measurement systems.

[0010] That is, the invention is based on the insight that the performance of a particular measurement system can be monitored by associating at one or more points in time at least one of its operating parameters with the operating parameters of at least a subset of the plurality of the measurement systems. Said operating parameters of at least the subset of the measurement systems thus act, in a sense, as reference operating parameters that are used for the association with the operating parameter of the particular measurement system of concern. That is, a so-called cross-sectional association and thus cross-sectional monitoring is performed, wherein the operating parameters of a plurality of measurements systems are considered at one or more points in time. Said operating parameters can thus be referred to as cross-sectional operating parameters. Unless stated otherwise, statements referring to a cross-sectional operating parameter preferably likewise apply to an operating parameter and vice versa.

[0011] Since the association is based on operating parameters of the measurement systems, no expert knowledge or the like is required to define criteria for an inferior or good performance. Instead, the invention is based on the insight that a majority of the measurement systems perform well and can thus serve as the basis for the evaluation of the performance of a particular measurement system of concern.

[0012] Thus, the operating parameters are preferably dynamic parameters, i.e. derived from the measurement systems.

[0013] It should be noted that one or more operating parameters can be considered for the particular measurement system. Unless stated otherwise, statements made with respect to one operating parameter preferably likewise apply to two or more operating parameters and vice versa.

[0014] Likewise, one or more operating parameters of at least the subset of measurement systems can be considered. Unless stated otherwise, statement made with respect to one operating parameter of at least the subset of measurement systems preferably likewise apply to two or more operating parameters of at least the subset of measurement systems and vice versa. Consequently, the plurality of operating parameters can comprise one or more operating parameters of each measurement system of at least the subset of measurement systems.

[0015] Unless stated otherwise, the monitoring can be based on the association of the operating parameter with the cross-sectional operating parameters of a subset of measurement systems or with the operating parameters of all measurement systems, i.e. with the cross- sectional operating parameters of the "entire" plurality of measurement systems. Unless stated otherwise, statements made with respect to the cross-sectional operating parameters of a subset of measurement systems preferably likewise apply to the cross-sectional operating parameters of all, i.e. the entire plurality of measurement systems and vice versa.

[0016] Moreover, the performance of a single measurement system of concern, i.e. of one particular measurement system, can be monitored, or the performance of two or more measurement systems of the plurality of measurement systems can be monitored. Statements made with respect to the monitoring of one or particular measurement system preferably likewise apply to the monitoring of two or more measurement systems and vice versa.

[0017] The plurality of cross-sectional operating parameters is preferably provided in a single storage space, for instance in a local storage of the measurement system and / or in a cloudbased database, e.g. in a Digital Twin.

[0018] Moreover, it is preferred that the plurality of cross-sectional operating parameters is stored with keys, in particular with a timestamp and a measurement system id, [timestamp, measurement system id].

[0019] The timestamp can be any point in time and corresponds to the point in time the cross- sectional operating parameters of the plurality of measurement systems were received.

[0020] The measurement system id is a unique identifier of a particular measurement system, e.g. a serial number of the measurement system, corresponding to the particular operating parameter.

[0021] The association of the operating parameter of the particular measurement system with the cross-sectional operating parameters of at least the subset of measurement systems is preferably performed for the same one or more timestamps of these operating parameters. For instance, the cross-sectional operating parameters of the plurality of measurement systems are preferably continuously stored in a storage space such as the single storage space mentioned before. At one or more points in time, for instance once daily, hourly, etc., the method preferably associates the operating parameter of the particular measurement system with the plurality of cross-sectional operating parameters of the plurality of measurement systems. These points in time can be predetermined points in time or can be related to an outcome of the association. For example, a mismatching association could lead to a next association being performed after a certain time. This means that the method of monitoring the performance of the measurement system is preferably performed continuously. Or in other words, the steps of receiving and associating the operating parameter of a particular measurement system with the cross-sectional operating parameters of at least the subset of the plurality of measurement systems are preferably performed continuously, i.e. repetitively.

[0022] A matching association between the operating parameter of the particular measurement system and the cross-sectional operating parameters of at least a subset of the measurement systems is preferably indicative of a good performance of the particular measurement system and a mismatching association between the operating parameter of the particular measurement system and the cross-sectional operating parameters of at least a subset of the plurality of measurement systems is preferably indicative of an inferior performance of the particular measurement system.

[0023] Examples of an inferior performance of the particular measurement system are an abnormal set parametrization of the particular measurement system, a wear or tear of components of the particular measurement system, a poor calibration, a contamination, electrical noise or interference for instance from external sources, inadequate maintenance, software issues, operator errors, etc.

[0024] Examples of a good performance of the particular measurement system thus are a normal set parametrization of the particular measurement system, absence of wear or tear of components of the particular measurement system, a proper calibration, no contamination, absence of electrical noise or interference for instance from external sources, adequate maintenance, absence of software issues, absence of operator errors, etc.

[0025] The operating parameter being associated with the cross-sectional operating parameters of at least a subset of measurement systems preferably means that the operating parameter of the particular measurement system is compared with the cross-sectional operating parameters of at least the subset of measurement systems.

[0026] The association such as the comparison between the cross-sectional operating parameters preferably takes into account tolerances, for instance measurement tolerances of the measurement systems such as noise.

[0027] An outcome of the association, i.e. a matching or a mismatching, preferably is at least one of a Boolean such as true or false, a numerical value or a numerical range of values.

[0028] The numerical value or numerical range of values preferably are the numerical result of the comparison between the cross-sectional operating parameters. The Boolean is preferably based on said numerical result.

[0029] The cross-sectional operating parameters are preferably represented by at least one numerical value and / or at least one alphanumeric character.

[0030] As will be explained in greater detail further below, during the association, the at least one numerical value and / or the at least one alphanumeric character of the operating parameter of the particular measurement system is preferably compared with the at least one numerical value and / or the at least one alphanumeric character of the cross-sectional operating parameters of at least the subset of measurement systems.

[0031] A matching association is preferably determined in the case of identical alphanumeric characters and / or identical numerical values, preferably while taking into account tolerances. A mismatching association is preferably determined in the case of differing alphanumeric characters and / or numerical values, preferably while taking into account tolerances.

[0032] The cross-sectional operating parameters are preferably based on at least one of: a measurement system setting of the measurement systems, a hardware component of the measurement systems, a measurement configuration of the measurement systems, a tuning variable of the measurement systems, or a readback value of the measurement systems.

[0033] The measurement system setting preferably configures the measurement system for specific applications.

[0034] Examples of measurement system settings are a software version or a firmware version of the measurement system.

[0035] The operating parameter is preferably at least one numerical value and / or at least one alphanumeric character that represents the measurement system setting such as the software version or the firmware version of the measurement system.

[0036] Hence, the plurality of cross-sectional operating parameters are in each case preferably at least one numerical value and / or at least one alphanumeric character that represent the measurement system setting such as the software version or the firmware version of the corresponding measurement system.

[0037] The hardware component is preferably a structural or physical part of the measurement system.

[0038] Examples of hardware components of the measurement system are a cartridge or a pump in the event of the measurement system comprising, for instance, a mass spectrometer.

[0039] The operating parameter is preferably at least one numerical value and / or at least one alphanumeric character that represents the hardware component such as a type or serial number of the cartridge or the pump.

[0040] Hence, the plurality of cross-sectional operating parameters are preferably in each case at least one numerical value and / or at least one alphanumeric character that represents the hardware component such as a type or serial number of the cartridge or the pump of the corresponding measurement system.

[0041] The measurement configuration preferably reflects a matching of the measurement system with a known standard or reference and / or reflects at least one of an accuracy, deviation, adjustment, or uncertainty of the measurement system.

[0042] Examples of measurement configurations of the measurement system are results of a calibration of the measurement system. For instance, in the event of the measurement system comprising a mass spectrometer conceivable measurement configurations are results of a m / z calibration or of an ion mobility calibration or of a digitizer calibration.

[0043] An m / z calibration preferably provides as a calibration result the coefficients for a transformation between time-of-flight and m / z value. The calculation is preferably performed based on a measurement with a well-known substance.

[0044] An ion mobility preferably provides as a calibration result the coefficients for a transformation between countervoltage and mobility value. The calculation is preferably performed based on a measurement with a well-known substance.

[0045] A digitizer calibration preferably provides as a calibration result an adjustment of a baseline of the measurement system, in particular of a component thereof, to zero by measuring the offset in Volts. In other words, the digitizer calibration preferably determines a digital noise and an offset of the measurement system, in particular of a component thereof.

[0046] The operating parameter is preferably at least one numerical value and / or at least one alphanumeric character that represents the measurement configuration such as a type or a coefficient or a score of a m / z calibration or a type or a coefficient or a score of an ion mobility calibration or an offset or a noise suppression threshold of a digitizer calibration of the measurement system.

[0047] Hence, the plurality of cross-sectional operating parameters is preferably in each case at least one numerical value and / or at least one alphanumeric character that represents the measurement configuration such as a type or a coefficient or a score of a m / z calibration or a type or a coefficient or a score of an ion mobility calibration or an offset or a noise suppression threshold of a digitizer calibration of the corresponding measurement system.

[0048] The tuning variable is preferably configured to tune at least one component, in particular at least one hardware component of the measurement system.

[0049] Examples of tuning variables of the measurement system are for instance a TOF voltage or a detector voltage in the event the measurement system comprising a mass spectrometer. The operating parameter is preferably at least one numerical value that represents the tuning variable such a numerical value corresponding to the TOF voltage or to the detector voltage of the measurement system.

[0050] Hence, the plurality of cross-sectional operating parameters is preferably in each case at least one numerical value that represents the tuning variable such a numerical value corresponding to the TOF voltage or to the detector voltage of the corresponding measurement system.

[0051] The readback value is preferably detectable by at least one component, in particular by at least one hardware component of the measurement system.

[0052] Examples of readback values of the measurement system are a temperature within the measurement system, a power consumption of a turbo pump, or a vacuum pressure within the measurement system.

[0053] The operating parameter is preferably at least one numerical value and / or at least one alphanumeric character that represents the readback value such a numerical value corresponding to the TOF voltage or to the detector voltage or to the vacuum pressure of the measurement system.

[0054] Hence, the plurality of cross-sectional operating parameters are preferably in each case at least one numerical value and / or at least one alphanumeric character that represents the tuning variable such a numerical value corresponding to the TOF voltage or to the detector voltage or to the vacuum pressure of the corresponding measurement system.

[0055] Although the examples above only refer to a measurement system comprising a mass spectrometer, it should be understood that the operating parameters can also be provided from other measurement systems configured to analyse a sample. Also, the operating parameters are not limited to the examples above, but a multitude of further operating parameters being associated with an operation of a measurement system are conceivable.

[0056] Thus, and as mentioned above, the cross-sectional operating parameter of a particular measurement system being associated with the cross-sectional operating parameters of at least a subset of the measurement systems preferably means that the cross-sectional operating parameter of the particular measurement system is compared with the cross- sectional operating parameters of at least the subset of measurement systems.

[0057] For example, and as will be explained in greater detail below, the operating parameter of the particular measurement system can be a tuning variable such as a detector voltage of the particular measurement system that is compared with the cross-sectional operating parameters of at least the subset of measurement systems being in each case a tuning variable in the form of a detector voltage of the corresponding measurement system, and wherein the performance of the particular measurement system is then assessed based on the outcome of said comparison.

[0058] A matching association preferably exists in the case of the operating parameter of the particular measurement system corresponding, preferably within any tolerances, to the cross-sectional operating parameters of at least the subset of the measurement systems, for instance the detector voltage of the particular measurement system corresponding to the cross-sectional detector voltages of the at least the subset of the measurement systems. A mismatching association preferably exists in the event that the operating parameter of the particular measurement system such as the detector voltage of the particular measurement system does not correspond to the cross-sectional operating parameters such as the cross- sectional detector voltages of at least the subset of the measurement systems. That is, in the above example an association, in particular a comparison, of the cross- sectional operating parameters is carried out for single operating parameters.

[0059] However, it is likewise conceivable that a combination of operating parameters is associated with one another. That is, two or more operating parameters of a corresponding measurement system of the plurality of measurement system can be combined into differences and / or ratios and / or other mathematical combinations thereof, and wherein the monitoring is preferably performed based on the association of a combination of two or more operating parameters of the particular measurement system with a combination of two or more cross-sectional operating parameters of measurement systems of at least a subset of the plurality of measurement systems.

[0060] For instance, the difference between two operating parameters of the particular measurement system can be compared with the differences between two cross-sectional operating parameters of each of the plurality measurement systems.

[0061] For example, differences between operating parameters in the form of tuning variables such as voltages can be determined for each measurement system, and wherein an association such as a comparison is performed for differences between two voltages:

[0062] Measurement system 1 : operating parameter 1 = 10 Volt, operating parameter 2 = 6 Volt, with the difference being 4 Volt

[0063] Measurement system 2: operating parameter 1 = 15 Volt, operating parameter = 11 Volt, with the difference being 4 Volt.

[0064] An association, in particular comparison of the combined operating parameters of measurement system 1 with the combined operating parameters of the measurement system 2 is matching.

[0065] It is furthermore conceivable that the plurality of cross-sectional operating parameters provides a threshold value or a threshold range against which the operating parameter of the particular measurement system is compared during the association, and wherein a matching or mismatching association is determined if the operating parameter of the particular measurement system is below or above the threshold value or inside or outside of the threshold range, for instance.

[0066] At least one threshold value and / or at least one threshold range is preferably determined from at least the subset of the plurality of cross-sectional operating parameters. The monitoring is preferably based on the association of the cross-sectional operating parameter of the particular measurement system with the threshold value and / or the threshold range.

[0067] The threshold value preferably is a numerical value. For instance, a threshold value of the plurality of cross-sectional operating parameters being detector voltages could be an average voltage of all detector voltages. In particular, the threshold value preferably is a numerical value of a statistical property determined in a statistical analysis of at least a subset of the plurality of cross-sectional operating parameters such as an average, a standard deviation, a median, or a kth-percentile such as the 10thor 90thpercentile, etc of the cross-sectional operating parameters.

[0068] The threshold range preferably is a numerical range of values. For instance, a threshold range of the plurality of cross-sectional operating parameters being detector voltages could be an average detector voltage and its standard deviation or a multiple of its standard deviation.

[0069] That is, the threshold range preferably is a numerical range of values based on a statistical property determined in a statistical analysis of at least a subset of the plurality of cross- sectional operating parameters such as an average, a standard deviation, a median, a gradient, or a kth-percentile such as the 10thor 90thpercentile, etc of the cross-sectional operating parameters.

[0070] The threshold range preferably has a lower threshold and an upper threshold that are based on the outcome of said statistical analysis, such as a threshold range being defined by the lower threshold and upper threshold [lower threshold; upper threshold] of [average value - n*standard deviation; average value + n*standard deviation], wherein n= 1 , 2, ... etc.

[0071] Hence, a matching association could be determined if the detector voltage of the particular measurement system is below or above the cross-sectional average value of the detector voltages of the measurement systems. Likewise, a matching association could be determined if the detector voltage of the particular measurement system is inside the range defined by the cross-sectional average value and the standard deviation of the detector voltages of the measurement systems.

[0072] Another example of a threshold range is a threshold range comprising a lower threshold being an average value of a flight tube temperature minus three times its standard deviation, and an upper threshold being the average value of the flight tube temperature plus three times its standard deviation.

[0073] The threshold values and / or threshold range are preferably redetermined continuously, e.g. once per day or once per hour. Consequently, the threshold values and / or threshold ranges are preferably dynamic and may adapt over time.

[0074] A subset of measurement systems of the plurality of measurement systems is preferably selected based on one or more cross-sectional operating parameters of said subset of measurement systems. The monitoring is preferably based on the association of at least one operating parameter of a particular measurement system of said subset of measurement system with the cross-sectional operating parameters of said subset of measurement systems.

[0075] That is, operating parameters can be used to select a subset of measurement systems from the plurality of measurement systems, and wherein only said selected measurement systems and their operating parameters are considered for the monitoring, in particular for the association of the operating parameters. In other words, the method enables to filter out measurement systems that shall not be considered in the monitoring.

[0076] For instance, operating parameters being based on the measurement system settings can be used to select or filter out a subset from the whole fleet of measurement systems before determining the threshold value or threshold range from the operating parameters. For example, the selection or filtering could be the determination of a threshold value or threshold range of operating parameters of measurement systems that comprise specific hardware components, e.g. an ion lens.

[0077] The threshold value(s) or threshold range(s) for the decision if an operating parameter of a particular measurement system is considered as mismatching and, for instance, as an outlier, are in each case preferably determined using the cross-sectional analysis, for instance cross-sectional statistics. In fact, as mentioned above and will be discussed in more detail below, the monitoring or association can be based on the value(s) of the operating parameters themselves or on their longitudinal values such as longitudinal average, their longitudinal gradient, their standard deviation or other statistical properties. At least one statistical cross-sectional operating parameter is preferably determined from the cross-sectional operating parameters of at least a subset of the plurality of measurement systems using cross-sectional statistics. The monitoring is preferably based on the association of the operating parameter of the particular measurement system with said statistical cross-sectional operating parameter.

[0078] That is, at least one statistical cross-sectional operating parameter can be determined from the cross-sectional operating parameters of at least a subset of the measurement systems using cross-sectional statistics, i.e. at least one statistical cross-sectional operating parameter is preferably determined from the cross-sectional operating parameters of at least a subset of the measurement systems at one or more points in time.

[0079] In other words, the cross-sectional operating parameters of at least a subset of the measurement systems are subjected to a statistical analysis at one or more points in time, i.e. subjected to a cross-sectional statistics, wherein statistical parameters such as an average, a standard deviation, a median, or a kth-percentile such as the 10thor 90thpercentile, etc. of said cross-sectional operating parameters are determined at one or more points in time.

[0080] Said statistical cross-sectional operating parameter can be used as a threshold value and / or can be used in a threshold range, and wherein the monitoring is based on the association of the operating parameter of the particular measurement system with said threshold value and / or the threshold range.

[0081] As an example, the operating parameters can be readback values such as TOF detector voltages. By performing a cross-sectional statistical analysis of the operating parameters, the average detector voltage and its standard deviation can be determined. These statistical properties can be used to define a threshold range. For instance, a conceivable threshold range could be based on the average detector voltage "plus" and "minus" three times its standard deviation, [average value - 3*standard deviation; average value + 3*standard deviation]. If the association, in particular comparison, of the operating parameter of the particular measurement system in the form of a detector voltage is outside of the threshold range, a mismatching association is determined and said operating parameter, i.e. said detector voltage, can be identified as an outlier.

[0082] For each measurement system of at least a subset of the plurality of measurement systems, at least one statistical longitudinal operating parameter is preferably determined from a plurality of operating parameters being received from the corresponding measurement system over time using longitudinal statistics. The monitoring is preferably based on the association of the operating parameter of the particular measurement system and / or of the statistical longitudinal operating parameter of the particular measurement system with the cross-sectional operating parameters being based on said statistical longitudinal operating parameters of at least a subset of the measurement systems.

[0083] That is, for each measurement system of at least a subset of the plurality of measurement systems at least one statistical longitudinal operating parameter can be determined from a plurality of operating parameters being received from said measurement systems over time using longitudinal statistics, i.e. for each measurement system, at least one statistical longitudinal operating parameter is preferably determined from repeated receives of a same operating parameter over time using longitudinal statistics.

[0084] In other words, two or more operating parameters that are received over time from a particular measurement system can be subjected to a statistical analysis, i.e. subjected to a longitudinal statistic, wherein longitudinal statistical parameters such as an average, a standard deviation, a median, or a kth-percentile such as the 10thor 90thpercentile, etc. of said operating parameters are determined over time.

[0085] The operating parameter of a particular measurement system and / or the statistical longitudinal operating parameter of a particular measurement system can be associated with the statistical longitudinal operating parameters of at least a subset of the plurality of measurement systems at one or more points in time. In other words, it is conceivable that in a first step longitudinal statistics is performed on the operating parameters of each measurement system of at least the subset of measurements system over time in order to determine for each of these measurement systems at least one statistical longitudinal operating parameter, and wherein in a second step, said statistical longitudinal operating parameters are associated at one or more points in time with the operating parameter of the measurement system of concern and / or the statistical longitudinal operating parameter of the measurement system of concern. In other words, in the second step, the statistical longitudinal parameters serve as the cross-sectional parameters in the association. Again in other words, the monitoring is based on a longitudinal statistics applied to the operating parameters in a first step, and wherein a cross-sectional analysis is performed in a second step. Hence, the method can be used for a longitudinal monitoring in addition to the cross- sectional monitoring.

[0086] The cross-sectional parameters based on said statistical longitudinal operating parameters of at least the subset of the measurement systems can be used as threshold values and / or can be used in threshold ranges, and wherein the monitoring is based on the association of the operating parameter of the particular measurement system and / or of the statistical longitudinal operating parameter of the particular measurement system with said threshold value and / or the threshold range.

[0087] For example, a threshold range could be based on the average longitudinal gradient over time of operating parameters being a turbo pump power consumption "plus" and "minus" two times its standard deviation of at least a subset of the measurement systems.

[0088] The operating parameter such as the turbo pump power consumption of a particular measurement system of concern or the statistical longitudinal operating parameter such as the average turbo pump power consumption of the particular measurement system of concern is then preferably associated, in particular compared with said threshold range, wherein it is determined whether said particular turbo pump power or average turbo pump power lies within or outside of the range.

[0089] It is also conceivable to base the monitoring on at least one statistical cross-sectional operating parameters being determined from the cross-sectional operating parameters using cross-sectional statistics. That is, and as just described above, for each measurement system of at least a subset of the plurality of measurement systems, at least one statistical longitudinal operating parameter is preferably determined from a plurality of operating parameters being received from the corresponding measurement system over time using longitudinal statistics. Then, at least one statistical cross-sectional operating parameter is determined from the cross-sectional operating parameters being based on, in particular corresponding to, said statistical longitudinal operating parameter using cross-sectional statistics. The monitoring is then based on the association of the operating parameter of the particular measurement system and / or of the statistical longitudinal operating parameter of the particular measurement system with said at least one statistical cross-sectional operating parameter being determined from the cross-sectional operating parameters using cross-sectional statistics. Instead of performing longitudinal statistics in a first step and a cross-sectional analysis in a second step as described above, the other way round is likewise conceivable. That is, it is conceivable to perform a cross-sectional analysis in a first step, and to then perform a longitudinal analysis in a second step.

[0090] Hence, at least one statistical longitudinal operating parameter is preferably determined from the cross-sectional operating parameters of at least a subset of the measurement systems received over time using longitudinal statistics. The monitoring is preferably based on the association of the operating parameter of the particular measurement system and / or of the statistical longitudinal operating parameter of the particular measurement system with said statistical longitudinal operating parameter.

[0091] In the following, some concrete examples of the various conceivable cross-sectional and longitudinal analyses performed by the method according to the invention are made for illustrative purposes only.

[0092] That is, the above table shows four examples of monitoring a performance of a measurement system according to the method of the present invention.

[0093] Namely, in the first example, longitudinal statistics is used to determine a statistical longitudinal operating parameter over time for one measurement system of concern, wherein said statistical longitudinal operating parameter is the average being determined as 1000.07 volts from the example values of 1000.00 volts, 999.90 volts and 1000.30 volts.

[0094] In the second example, a statistical cross-sectional operating parameter is determined from the cross-sectional operating parameters of three measurement systems using cross- sectional statistics, wherein said statistical cross-sectional operating parameter is the average being determined as 1002.33 volts from the example values of 100.00 volts, 1004.00 volts and 1003.00 volts.

[0095] In the third example, for each measurement system out of three measurement systems, a statistical longitudinal operating parameter is determined from three operating parameters being received from the corresponding measurement system over time using longitudinal statistics. In the present example, said statistical longitudinal operating parameter of the first measurement system is the average being determined as 1000.07 volts from the example values of 1000.00 volts, 999.90 volts and 1000.30 volts. Said statistical longitudinal operating parameter of the second measurement system is the average being determined as 999.77 volts from the example values of 997.80 volts, 999.30 volts and 1002.20 volts. Said statistical longitudinal operating parameter of the third measurement system is the average being determined as 1000.07 volts from the example values of 1000.00 volts, 999.90 volts and 1000.30 volts. Then, in a second step cross-sectional statistics is performed on these statistical longitudinal operating parameters, wherein the statistical cross-sectional operating parameter being the average of 999.97 volts is determined from the statistical longitudinal operating parameters 1000.07 volts, 999.77 volts and 1000.07 volts.

[0096] In the fourth example, the statistical cross-sectional operating parameter is determined from three measurement systems in a first step. In a second step, longitudinal statistics is performed on said statistical cross-sectional operating parameter over time. In particular, in the first step at a first point in time, the statistical cross-sectional operating parameter 999.27 volts is determined from the operating parameter values 1000.00 volts, 997.80 volts and 1000.00 volts. At a second point in time, the statistical cross-sectional operating parameter 999.47 volts is determined from the operating parameter values 999.20 volts, 999.30 volts and 999.90 volts. At a third point in time, the statistical cross-sectional operating parameter 1000.00 volts is determined from the operating parameter values 1000.30 volts, 1002.20 volts and 997.50 volts. Then, in a second step, longitudinal statistics is performed on these statistical cross-sectional operating parameters, wherein the statistical longitudinal operating parameter being the average and 999.58 volts is determined from the statistical cross-sectional operating parameters 999.27 volts, 999.47 volts and 1000.00 volts.

[0097] The cross-sectional operating parameters can be represented by at least one alphanumeric character. In this case, at least one target operating parameter is preferably determined from the cross-sectional operating parameters of at least a subset of measurement systems if 90% or more, for instance 95% or more, of said cross-sectional operating parameters are represented by the same at least one alphanumeric character. The monitoring is preferably based on the association of the operating parameter of the particular measurement system with said target operating parameter.

[0098] That is, it is conceivable that the operating parameters are represented by one or more alphanumeric characters, for instance if they are measurement system settings such as a software version or firmware version. In this case, it is not possible to determine a numerical threshold value or a numerical threshold range.

[0099] Instead, it is conceivable to determine a target operating parameter for these operating parameters based on the cross-sectional data. For example, if more than 90% or more than 95% of the measurement systems have the same alphanumerical character(s) representing the operating parameters such as a measurement system setting, said alphanumeric character(s) can be defined as the target operating parameter. It should be noted that other target criteria than said 90% or 95% can of course likewise be used.

[0100] The monitoring can then be based on the association of the operating parameter of the particular measurement system of concern with said target operating parameter.

[0101] For example, after a global software upgrade of the measurement systems, the target operating parameter could be a software version 2.0. If the particular measurement system of concern is still running with software version 1.0, the association with the target operating parameter is mismatching and the outdated software version 1.0 could be identified for instance as an outlier. The measurement systems are preferably of a same type, and / or the operating parameter being associated with the cross-sectional operating parameters are preferably of a same type.

[0102] That is, the measurement systems preferably are of a same type such as all being mass spectrometer.

[0103] Additionally or alternatively, the operating parameters being associated with one another are preferably of a same type. For instance, the operating parameters could be of a same type such as being measurement system settings, a voltage, a temperature, etc.

[0104] At least one output signal is preferably generated in the event of a mismatching association between the operating parameter of the particular measurement system and the cross- sectional operating parameters of at least the subset of the measurement systems. The output signal is preferably communicated to a user of the particular measurement system. Additionally or alternatively, the output signal preferably comprises indications of the mismatching association such as the indication of the mismatching operating parameter. Additionally or alternatively, the output signal is preferably configured as an input signal to the particular measurement system and configured to control an operation of the particular measurement system.

[0105] That is, in the event of a mismatching association between the operating parameter(s) of the particular measurement system and the operating parameters of at least the subset of the plurality of measurement systems, the method preferably generates an output signal which, for instance, is communicated to the user and thus informs the user on the mismatching. For instance, the output signal can be configured as an automatic notification or warning to the user. Additionally or alternatively, the output signal could comprise a recommendation or a proposed solution to the user.

[0106] For example, in the event of a mismatching operating parameter of the particular measurement system relating to an outdated data acquisition software version or firmware version of said measurement system, the output signal could notify the user that the data acquisition software version or the firmware version has changed.

[0107] It should be noted that such an output signal can likewise be generated and communicated in the event of a matching association between the operating parameters.

[0108] The operating parameters are preferably provided from measurement systems comprising a mass spectrometer, optionally connected to an LC system.

[0109] That is, the method according to the invention is preferably configured and / or used to monitor the performance of a measurement system comprising a mass spectrometer.

[0110] To this end various mass spectrometers and mass spectrometry techniques are conceivable. For instance, the mass spectrometer can comprise a time-of-flight (TOF) analyzer, for instance with an axial TOF arrangement, or a trapped ion mobility time-of-flight (timsTOF) mass spectrometry. For example, the measurement system could be configured for targeted MALDI imaging or general MALDI imaging. However, it should be noted that various other mass spectrometers and mass spectrometry techniques or ionization techniques such as ESI (electrospray ionization), etc., are likewise conceivable and are well-known in the art.

[0111] In another aspect, a measurement system for analysing a sample, preferably a mass spectrometer, is provided. The measurement system comprises a processor comprising instructions which, when carried out by the processor, causes the processor to carry out the method as described above.

[0112] Any explanations made herein regarding the method preferably likewise apply to the measurement system comprising the processor configured to carry out the method and vice versa.

[0113] BRIEF DESCRIPTION OF THE DRAWINGS

[0114] Preferred embodiments of the invention are described in the following with reference to the drawings, which are for the purpose of illustrating the present preferred embodiments of the invention and not for the purpose of limiting the same. In the drawings,

[0115] Fig. 1 shows a schematics of the method for monitoring the performance of a measurement system according to the invention;

[0116] Fig. 2 shows an exemplary mapping of operating parameters of a plurality of measurement systems over time and over the measurement systems used in the method according to the invention;

[0117] Fig. 3 shows an exemplary record of cross-sectional operating parameters of a plurality of measurement systems and the determination of a threshold range for an association according to the invention;

[0118] Fig. 4 shows an exemplary association of an operating parameter of a particular measurement system with the threshold range determined from the cross- sectional parameters from the plurality of measurement systems according to figure 3;

[0119] Fig. 5 shows another exemplary record of cross-sectional operating parameters of a plurality of measurement systems and the determination of a threshold range for an association according to the invention;

[0120] Fig. 6 shows an exemplary association of an operating parameter of a particular measurement system with the threshold range determined from the cross- sectional operating parameters of the plurality of measurement systems according to figure 5;

[0121] Fig. 7 shows another exemplary mapping of operating parameters over time and over the measurement systems used in the method according to the invention including the detection of an outlier.

[0122] DESCRIPTION OF PREFERRED EMBODIMENTS

[0123] Aspects of the method according to the invention are now illustrate with reference to the figures.

[0124] That is, and as illustrated in figure 1 , the method according to the invention is based on the insight that the performance of a particular measurement system can be monitored based on a plurality of operating parameters that are received from a plurality of measurement systems at one or more points in time, i.e. cross-sectional operating parameters are received, wherein the cross-sectional operating parameters are associated with an operation of the measurement systems. In the depicted example, the measurement systems are MS / LC devices that continuously upload operating parameters via an internet connection to a storage space in the form of a cloud based digital twin database.

[0125] In the depicted example, operating parameters over time of the measurement system of concern are subjected to longitudinal statistics, whereby a statistical longitudinal operating parameter is determined.

[0126] The cross-sectional operating parameters of all or a subset of measurement systems are subjected to a cross-sectional analysis, in particular a cross-sectional statistical analysis so as to obtain one or more thresholds, in particular threshold values or threshold ranges, see Step 2.

[0127] Subsequently, in a Step 3, the statistical longitudinal operating parameter is associated, in particular compared, with the threshold(s) determined in Step 2.

[0128] Depending on an outcome of the association, in particular whether a matching or mismatching association is determined in Step 3, an output signal such as a user notification is issued, see Step 4.

[0129] Figure 2 shows an exemplary mapping of operating parameters of several measurement systems over several points in time, i.e. cross-sectionally, as well as over time, i.e. longitudinally. The operating parameters correspond here to measurement configurations of the measurement systems in the form of a digitizer offset. On one axis, the mapping shows the longitudinal monitoring of the operating parameter of one measurement system, i.e. the digitizer offset over time. On the other axis, the mapping shows a cross-sectional monitoring of the operating parameters over the measurement systems at several points in time.

[0130] Figure 3 shows an example of a performance monitoring based on operating parameters being a flight tube temperature. In fact, a threshold range comprising an upper threshold being the median value of the flight tube temperatures plus 2 times its standard deviation and a lower threshold being the median of the flight tube temperatures value minus 2 times its standard deviation is determined from a cross-sectional statistical analysis of the operating parameters of a subset or of all measurement systems.

[0131] The threshold range determined in the example of figure 3 can be applied for a longitudinal analysis of the operating parameter of every single measurement system, see figure 4. That is, figure 4 shows an example of a performance monitoring of a particular measurement system of concern, here of the device no. 7, based on a longitudinal monitoring of an operating parameter being a flight tube temperature that is associated, in particular compared, with the threshold range comprising an upper threshold being the median value of the flight tube temperatures plus 2 times its standard deviation and a lower threshold being the median of the flight tube temperatures value minus 2 times its standard deviation that were determined from a cross-sectional statistical analysis of the operating parameters of the subset or of all measurement systems according to figure 3. When the operating parameter, i.e. the flight tube temperature, of the measurement system of concern is outside of the threshold range, a mismatching association or an outlier is determined. In the depicted example this occurs at the time after 16:48, wherein said mismatching or outlier is indicative of a failure of a cooling unit that is supposed to cool the flight tube of the measurement system of concern.

[0132] Figure 5 shows an example of a threshold range being determined from leak flow rates as operating parameters of the measurement system. In particular, the leak flow rates of a plurality of measurement systems are subjected to a cross-sectional statistical analysis, wherein an average and its standard deviation were determined. The average minus two times the standard deviation was set as lower threshold and the average plus two times the standard deviation was set as upper threshold.

[0133] The threshold range determined in the example of figure 5 can be applied for a longitudinal analysis of the operating parameter of every single measurement system, see figure 6. That is, figure 6 shows an example of a performance monitoring of a measurement system of concern, here of the LC device with serial number 1018, based on a longitudinal monitoring of an operating parameter being the leak flow rate that is associated, in particular compared, with the threshold range comprising an upper threshold being the average value of the leak flow rate plus 2 times its standard deviation and a lower threshold being the average value of the leak flow rate minus 2 times its standard deviation that were determined from a cross- sectional statistical analysis of the operating parameters of a subset or of all measurement systems. When the operating parameter, i.e. the leak flow rate, of the measurement system of concern is outside of the threshold range, a mismatching association or an outlier is determined and a corresponding output signal is generated.

[0134] Hence, the method according to the invention provides for an automatic and remote upload and monitoring of operating parameters of measurement systems that allow immediate identification of outliers or abnormalities and thus of an inferior performance of a measurement system.

[0135] The operating parameters are dynamic parameters such that criteria for their association, in particular thresholds for outliers, are dynamically determined for instance by statistics from the fleet of measurement systems. Consequently, there is need for a manual definition and maintenance of thresholds nor of a documentation. In particular, no specific and explicit definition of threshold values or threshold ranges are required. The method is based on the insight that the majority of measurement systems have been adjusted by experts and that an inferior condition such as defective hardware components are an exception.

[0136] Furthermore, the method according to the invention enables to the operation of measurement systems of inferior performance without expert knowledge of the user. The measurement systems are continuously monitored, and the user will be notified in case of mismatching operating parameters.

[0137] Figure 7 shows an exemplary mapping of operating parameters of several measurement systems over several points in time, i.e. cross-sectionally, as well as over time, i.e. longitudinally. The operating parameters correspond here to the turbo pump power consumption in % of the measurement systems. On one axis, the mapping shows the longitudinal monitoring of the operating parameter of one measurement system, i.e. the turbo power pump consumption in % over time. On the other axis, the mapping shows a cross-sectional monitoring of the operating parameters over the measurement systems at several points in time. In the depicted example, in a first step, for each measurement system a statistical longitudinal operating parameter is determined from the operating parameters over time using longitudinal statistics. Here, said statistical longitudinal operating parameter corresponds to the slope that is determined as 0.46 % for the first measurement system, as 0.60 % for the second measurement system, as 2.39 % for the third measurement system, as 0.58 % for the fourth measurement system, and as 0.39 % for the fifth measurement system. Then, in a second step, cross-sectional statistics is performed over these statistical longitudinal operating parameters, whereby the average of the slopes is determined as 0.89 %. Figure 7 illustrates that the value 0.89 is used as an upper threshold for detecting device 3 as an outlier.

Claims

1. 25CLAIMS1. A computer-implemented method for monitoring a performance of a measurement system configured to analyse a sample, wherein the method comprises the steps of:- Receiving a plurality of cross-sectional operating parameters being associated with an operation of a plurality of measurement systems configured to analyse a sample, and- Monitoring a performance of at least one particular measurement system of said plurality of measurement systems based on an association of at least one operating parameter of said particular measurement system with the cross-sectional operating parameters of at least a subset of the measurement systems.

2. The method according to claim 1 , wherein the operating parameter of said particular measurement system being associated with the cross-sectional operating parameters of at least the subset of measurement systems corresponds to a comparison of the operating parameter of the particular measurement system with the cross-sectional operating parameters of at least the subset of measurement systems.

3. The method according to claim 1 or 2, wherein the cross-sectional operating parameters are represented by at least one numerical value and / or at least one alphanumeric character.

4. The method according to claim 3, wherein the at least one numerical value and / or the at least one alphanumeric character of the operating parameter of the particular measurement system and the at least one numerical value and / or the at least one alphanumeric character of the cross-sectional operating parameters of at least the subset of measurement systems are compared with each other.

5. The method according to claim 4, wherein a matching association is determined in the case of identical alphanumeric characters and / or identical numerical values, preferably while taking into account tolerances, and a mismatching association is determined in the case of differing alphanumeric characters and / or numerical values, preferably while taking into account tolerances.

6. The method according to any one of the preceding claims, wherein an outcome of the association is at least one of a Boolean such as true or false, a numerical value or a numerical range of values.

7. The method according to claim 6, wherein the numerical value or numerical range of values are the numerical result of the comparison between the operating parameter of the particular measurement system and the cross-sectional operating parameters of at least the subset of measurement systems, and wherein the Boolean is preferably based on said numerical result.

8. The method according to any one of the preceding claims, wherein the cross- sectional operating parameters are based on at least one of: a measurement system setting of the measurement systems, a hardware component of the measurement systems, a measurement configuration of the measurement systems, a tuning variable of the measurement systems, or a readback value of the measurement systems.

9. The method according to any one of the preceding claims, wherein a matching association between the operating parameter of the particular measurement system and the cross-sectional operating parameters of at least the subset of the measurement systems is indicative of a good performance of the particular measurement system.

10. The method according to claim 9, wherein a good performance of the particular measurement system corresponds to at least one of: a normal set parametrization of the particular measurement system, absence of wear or tear of components of the particular measurement system, a proper calibration of the particular measurement system, no contamination of the particular measurement system, absence of electrical noise or interference for instance from external sources in the particular measurement system, adequate maintenance of the particular measurement system, absence of software issues in the particular measurement system, or absence of operator errors in the particular measurement system.11 . The method according to any one of the preceding claims, wherein a mismatching association between the operating parameter of the particular measurement system and the cross-sectional operating parameters of at least the subset of the plurality of measurement systems is indicative of an inferior performance of the particular measurement system.

12. The method according to claim 11 , wherein an inferior performance of the particular measurement system is at least one of: an abnormal set parametrization of the particular measurement system, a wear or tear of components of the particular measurement system, a poor calibration of the particular measurement system, a contamination of the particular measurement system, electrical noise or interference for instance from external sources in the particular measurement system, inadequate maintenance of the particular measurement system, software issues in the particular measurement system, or operator errors in the particular measurement system.

13. The method according to any one of the preceding claims, wherein at least one threshold value and / or at least one threshold range is determined from at least the subset of the plurality of cross-sectional operating parameters, and wherein the monitoring is based on the association of the cross-sectional operating parameter of the particular measurement system with the threshold value and / or the threshold range, and wherein a matching or mismatching association is preferably determined if the operating parameter of the particular measurement system is below or above the threshold value and / or inside or outside of the threshold range.

14. The method according to any one of the preceding claims, wherein a subset of measurement systems of the plurality of measurement systems is selected based on one or more cross-sectional operating parameters of said subset of measurement systems, and wherein the monitoring is based on the association of at least one operating parameter of a particular measurement system of said subset of measurement system with the cross-sectional operating parameters of said subset of measurement systems.

15. The method according to any one of the preceding claims, wherein at least one statistical cross-sectional operating parameter is determined from the cross-sectional operating parameters of at least a subset of the plurality of measurement systems using cross-sectional statistics, and wherein the monitoring is based on the association of the operating parameter of the particular measurement system with said statistical cross-sectional operating parameter.

16. The method according to any one of the preceding claims, wherein, for each measurement system of at least a subset of the plurality of measurement systems, at least28 one statistical longitudinal operating parameter is determined from a plurality of operating parameters being received from the corresponding measurement system over time using longitudinal statistics, and wherein the monitoring is based on the association of the operating parameter of the particular measurement system and / or of the statistical longitudinal operating parameter of the particular measurement system with the cross-sectional operating parameters being based on said statistical longitudinal operating parameters of at least a subset of the measurement systems.

17. The method according to claim 16, wherein the monitoring is based on the association of the operating parameter of the particular measurement system and / or of the statistical longitudinal operating parameter of the particular measurement system with at least one statistical cross-sectional operating parameter being determined from the cross- sectional operating parameters using cross-sectional statistics.

18. The method according to any one of the preceding claims, wherein at least one statistical longitudinal operating parameter is determined from the cross-sectional operating parameters of at least a subset of the measurement systems received over time using longitudinal statistics, and wherein the monitoring is based on the association of the operating parameter of the particular measurement system and / or of the statistical longitudinal operating parameter of the particular measurement system with said statistical longitudinal operating parameter.

19. The method according to any one of the preceding claims, wherein the cross- sectional operating parameters are represented by at least one alphanumeric character, wherein at least one target operating parameter is determined from the cross- sectional operating parameters of at least a subset of measurement systems if 90% or more, for instance 95% or more, of said cross-sectional operating parameters are represented by the same at least one alphanumeric character, and wherein the monitoring is based on the association of the operating parameter of the particular measurement system with said target operating parameter.

20. The method according to any one of the preceding claims, wherein the measurement systems are of a same type, and / or wherein the operating parameter being associated with the cross-sectional operating parameters are of a same type.2921. The method according to any one of the preceding claims, wherein at least one output signal is generated in the event of a mismatching association between the operating parameter of the particular measurement system and the cross-sectional operating parameters of at least the subset of measurement systems, and wherein at least one of:- the output signal is communicated to a user of the particular measurement system,- the output signal comprises indications of the mismatching association such as the indication of the mismatching operating parameter, or- the output signal is configured as an input signal to the particular measurement system and configured to control an operation of the particular measurement system.

22. A measurement system for analysing a sample, preferably a mass spectrometer, wherein the measurement system comprises a processor comprising instructions which, when carried out by the processor, causes the processor to carry out the method according to any one of the preceding claims.

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