GB2573336A

GB2573336A - Method for self-diagnostics of independent machine components

Info

Publication number: GB2573336A
Application number: GB1807368.4A
Authority: GB
Inventors: Tahedl Harald; Fritze Roland; Retzlaff Rüdiger; Wienhold Tobias
Original assignee: Stratec Biomedical AG
Current assignee: Stratec Biomedical AG
Priority date: 2018-05-04
Filing date: 2018-05-04
Publication date: 2019-11-06
Also published as: GB201807368D0

Abstract

A modular medical apparatus 14, e.g. an in vitro diagnostic (IVD) device, is self-tested for faults using sensors and actuators already present in the modules 2 of the apparatus, which are used to perform test runs to check for abnormal operation in the device. The actuators may be e.g. pumps or stepper motors, Peltier elements, RFID transmitters, heaters, valves, fans, or lifting magnets. The sensors may be photocells, Hall sensors, temperature sensors, acoustic or optical sensors, or sensors for capacitance, pressure, flow, or fill level. The sensors may also be rotary or linear encoders or RFID receivers. The modular apparatus may include pipettors, pumps, modules for washing reaction vessels, thermostat modules, or modules for measuring reaction strength. Test sequences may be repeated over time and the data compared to indicate an impending fault.

Description

[0001] The present invention is directed at a method and device for self-diagonstics of device components as described in the independent patent claims.

Brief description of the related art [0002] Complex machines and devices, and in particular medical devices, e.g. IVD devices for the processing of diagnostic assays, generally consist of a large number of individual device components, so-called subassemblies. These subassemblies - or modules for short - can cause errors during operation, e.g. caused by wear. Such errors lead to failure of the module and thus of the device or machine. If the failure of a module can be predicted, maintenance interventions can be planned and the customer can be warned of an impending device failure. In addition, in a service operation because of the unknown cause of error, components are replaced successively. If a component replacement does not resolve the error, additional components are replaced. This increases maintenance costs and spare parts costs, which are undesirable and detrimental.

[0003] A common approach to this problem is the continuous monitoring of device and module data, so-called telemetry data, which occurs in the context of normal operation. These data are continuously analyzed for error detection using machine learning models. However, these data were not specifically developed and collected for the prediction of failures, but accrue during the intended use of the device. As a result, they are usually subject to large fluctuations according to the use of modules and devices in the field and can only accidentally be connected with failures. The nature of the telemetry data and the timing of their collection has not been i

developed and optimized for the prediction of errors. Correlation with errors is random and the development of high-quality predictive models e.g. machine learning makes it harder and less likely.

Object of the invention [0004] The problem to be solved, therefore, is to provide a device and a method for secure, easy and reliable prediction and accurate identification of the failed module, in order to reduce maintenance costs.

Summary of the invention [0005] This problem is solved by the features and elements of the method and device as described in the independent claims, while meaningful embodiments are described by the features and elements of the dependent claims.

[0006] What is proposed according to the invention is a method for self-diagnostics of independent modules forming a diagnostic device, wherein actuators and sensors already installed in a specific module are used to provide special self-diagnostic data in test runs, specifically describing the performance and quality of the module.

[0007] The generated data can be analyzed and from this data single characteristics, which are decisive for the function of the specific module, can be determined.

[0008] The self-diagnostic data can be collected during reliability tests of the specific module that take place during development, and which may be correlated with errors occurring there.

[0009] The relevant parameters can be determined in a separate, dedicated diagnostic run for each specific module.

[0010] Fault exclusion for the device can be executed by running more than one module in parallel to produce self diagostic data, wherein the collection of self diagnostic data may be carried out under constant and defined conditions.

[0011] The self-diagnostic runs for each specific module can be repeated periodically with the same motion patterns, to enable the comparison of the measured values with one another, to simplify the identification of data trends.

[0012] Different modules of the same series can be tested with the same motion pattern, to provide comparability of the respective data obtained.

[0013] The self-diagnostic runs can be performed before completion of the device formed by those modules.

[0014] The self-diagnostic runs can be performed on a module tester.

[0015] According to the invention specific modules having a certain mechanical functionality, and being equipped with module-specific electronics as well as having a module-specific firmware (embedded software) installed, may be provided with functionality necessary for operation in the device in which the module is to be installed and for being controlled by higherlevel units.

[0016] Modules may be chosen from the group including pumps, air-based pipettors, liquidbased pipettors, modules for washing reaction vessels during diagnostic analysis, modules for thermostating reaction vessels during diagnostic analysis, and modules for measuring reaction strength during diagnostic analysis.

[0017] These modules can be designed and programmed to perform motion or general process steps as required in the device for the automated processing of diagnostic assays.

[0018] The functionality of the modules may be expanded to execute module-specific selfdiagnostic commands.

[0019] The self-diagnostic commands can consist of proprietary, module-specific movement pattern sequences in which the sensors and actuators which are present in the module can be used to determine quality and performance parameters in a targeted manner.

[0020] The performance parameters can be sent as part of the self-diagnostic command together with other parameters such as number of cycles, module age, or error history to a higher authority for storage and editing for later analysis.

[0021] For this purpose, z. B. statistical methods and / or feature engineering can be used. Based on the analysis results or the processed data the development of predictive models takes place, for example using physical models, correlations, extrapolation, knowledge-based models, and machine learning.

[0022] Over the life of the devices in the field, self-diagnostic data is continuously collected. These can be analyzed with the help of the developed predictive models to derive a prognosis about the future state of the modules. Here, in particular, the prediction of failures is of interest to derive preventive maintenance measures (predictive maintenance). This allows maintenance and service operations as well as spare parts logistics to be planned more precisely, thus reducing operating and service costs.

[0023] The self-diagnostic data may continuously be recorded for specific modules over the operating time of the devices in the field.

[0024] Self-diagnostic commands can be carried out periodically at points in time, in which the modules are not used for the assay processing in the device.

[0025] Beyond the sensors and actuators which are necessary and already integrated in the specific module for the main tasks thereof, additional dedicated sensors may be provided to improve the precision of the diagnosis further.

[0026] Actuators may be chosen from the group including but not limited to stepper motors, EC and DC motors, Peltier elements, RFID transmitters, Heaters, Valves, Fans, lifting magnets, and Pumps.

[0027] Sensors may be chosen from the group inculding, but not limited to, photocells, Hall sensors, Temperature sensors, Acoustic sensors, Optical sensors, Sensors for measuring capacitances, pressures, flows, fill levels, Rotary Encoders, Linear Encoders, and RFID receivers.

[0028] Still other aspects, features, and advantages of the present invention are readily apparent from the following detailed description, simply by illustrating a preferable embodiments and implementations. The present invention is also capable of other and different embodiments and its several details can be modified in various obvious respects, all without departing from the spirit and scope of the present invention. Accordingly, the drawings and descriptions are to be regarded as illustrative in nature, and not as restrictive. Additional objects and advantages of the invention will be set forth in part in the description which follows and in part will be obvious from the description or may be learned by practice of the invention.

Summary of the figures [0029] For a more complete understanding of the present invention and the advantages thereof, reference is now made to the following description and the accompanying drawings, in which:

FIG. 1 - FIG. 3 show a schematic representation of the relationship of module, self-diagnostic command, data collection and prediction according to the invention

Detailed description of the invention [0030] In the following, samples of actual self-diagnostic runs in a module are given.

[0031] In a self-diagnostic run to assess motion play in a module for circular or straight movements, power is transmitted mechanically to a component via a drive so that it moves in a circular or linear manner. Typical applications here are circular movements of a rotor or linear movements of a component triggered by a motor and a mechanism for transmitting the force (e.g., belt, gear or spindle) and finally the resulting motion.

[0032] The movement of the motor does not directly lead into the movement of the component due to various causes (mechanical play by tolerances, gear play, torsion, compression,...). This mechanical play or slippage can increase over the lifetime due to wear and thus lead to positioning inaccuracies and eventually to total failure of the module.

[0033] The goal is here is the determination of a diagnostic parameter, which describes the movement play or the mechanical slip of a movement. This diagnostic parameter is a measure of the positioning accuracy of a movement. The diagnostic parameter can be determined, for example, by moving from left / right (or for circular movements in / counterclockwise) to a detection mark, such as a light barrier, and then deliberately driving it over a defined path. Subsequently, it is driven in the opposite direction from the detection mark (for example, light barrier). The length of this movement is e.g. determined via encoder signals. A change of these measured movement lengths over time is a measure of an increasing movement play and a resulting poorer positioning accuracy.

[0034] In a another self-diagnostic run to assess minimum voltage to effect a movement, power is transmitted mechanically to a component via a drive so that it moves in a circular or straight manner. Typical applications here are circular movements of a rotor or rectilinear movements of a component triggered by a motor and a mechanism for transmitting the force (e.g., belt, gear or spindle) and finally the resulting motion.

[0035] The power of the engine must be large enough to overcome the drag due to friction. The friction can increase over the lifetime due to wear and thus lead to positioning inaccuracies and eventually to total failure of the module.

[0036] The aim is to determine the diagnostic parameter that describes the friction that must be overcome mechanically to trigger a movement. This diagnostic parameter is a measure of the stiffness of a movement. The diagnostic parameter may be determined, for example, by incrementally or iteratively increasing the torque of the motor until a movement is detectable. The minimum torque that is needed to just trigger the movement can then be used as a measure of the stiffness. The torque of a motor can be changed, for example, by changing the current (in stepper motors) or via pulse width modulation in DC/AC motors. By reaching a light barrier position, it can be determined, for example, whether a movement has still been carried out completely.

[0037] In Fig. 1 a module 2, such as a pump, is analyzed for potential errors and failures, such as blockades, step loss or positioning inaccuracies, using the existing actuators and sensors of module 2. In step 4 a self-diagnostic command specific for module 2 is triggered, which in this case is: Drive the drive of module 2 to a target with ever-decreasing energization until step loss is detected and use the value for the lowest current level at which no step loss was just detected as a diagnostic parameter A.

[0038] In Fig. 2 a reliability testing (endurance run) is shown schematically, in which a regular execution of the diagnostic command as in Fig. 1 is performed and diagnostic parameter A determined. As can be seen in graph 6 the endurance runs are repeated until a phase of an obvious pattern 8 is reached until the motor of module 2 fails at moment 10 followed by a phase of motor loss 9.. The diagnostic parameter A is analyzed of over time until failure through feature Engineering, modeling record, use of appropriate data analytics techniques (e.g. clustering, random forest, gradient boosting, ...), and a predictive model 12 from parameter A for the failure is developed.

[0039] Finally, in Fig. 3 a schematic representation of performing self-diagnostic tests during operation in a device 14, incorporating module 2, in the field is shown. In this case too a regular execution of the diagnostic command and determination of the diagnostic parameter A takes place. Furthermore, an analysis of the life data of parameter A using the predictive model is performed to predict an error before failure.

[0040] The foregoing description of the preferred embodiment of the invention has been presented for purposes of illustration and description. It is not intended to be exhaustive or to limit the invention to the precise form disclosed, and modifications and variations are possible in light of the above teachings or may be acquired from practice of the invention. The embodiment was chosen and described in order to explain the principles of the invention and its practical application to enable one skilled in the art to utilize the invention in various embodiments as are suited to the particular use contemplated. It is intended that the scope of the invention be defined by the claims appended hereto, and their equivalents. The entirety of each of the aforementioned documents is incorporated by reference herein.

Claims

1. Method for self-diagnostics of independent modules forming a diagnostic device, wherein actuators and sensors already installed in a specific module are used to provide special self-diagnostic data in test runs, specifically describing the performance and quality of the module.

2. Method according to claim 1, wherein the generated data is analyzed and from this data single characteristics, which are decisive for the function of the specific module, are determined.

3. Method according to claims 1 or 2, wherein the self-diagnostic data is collected during reliability tests of the specific module that take place during development, and which are correlated with errors occurring there.

4. Method according to claims 1 to 3, wherein the relevant parameters are determined in a separate dedicated diagnostic run for each specific module.

5. Method according to any of the preceding claims, wherein fault exclusion for the device is executed by running more than one module in parallel to produce self diagostic data, wherein the collection of self diagnostic data is carried out under constant and defined conditions.

6. Method according to any of the preceding claims, wherein the self-diagnostic runs for each specific module are repeated periodically with the same motion patterns, to enable the comparison of the measured values with one another, to simplify the identification of data trends.

7. Method according to any of the preceding claims, wherein different modules of the same series are tested with the same motion pattern, to provide comparability of the respective data obtained.

8. Method according to any of the preceding claims, wherein the self-diagnostic runs are performed before completion of the device formed by those modules.

9. Method according to claim 8, wherein the self-diagnostic runs are performed on a module tester.

10. Method according to any of the preceding claims, wherein specific modules having a certain mechanical functionality, and being equipped with module-specific electronics as well as having a module-specific firmware (embedded software) installed, are provided with functionality necessary for operation in the device in which the module is to be installed and for being controlled by higher-level units.

11. Method according to any of the preceding claims, wherein modules are chosen from the group including pumps, air-based pipettors, liquid-based pipettors, modules for washing reaction vessels during diagnostic analysis, modules for thermostating reaction vessels during diagnostic analysis, and modules for measuring reaction strength during diagnostic analysis.

12. Method according to claims 11, wherein these modules are designed and programmed to perform motion or general process steps as required in the device for the automated processing of diagnostic assays.

13. Method according to claims 12, wherein the functionality of these modules is expanded to execute module-specific self-diagnostic commands.

14. Method according to claim 13, wherein the self-diagnostic commands consist of proprietary, module-specific movement pattern sequences in which the sensors and actuators that are present in the module are used to determine quality and performance parameters in a targeted manner.

15. Method accordingt to claim 14, wherein the performance parameters are sent as part of the self-diagnostic command together with other parameters such as number of cycles, module age, or error history to a higher authority for storaged and editing for later analysis.

16. Method according to any of the preceding claims, wherein self-diagnostic data are continuously recorded for specific modules over the operating time of the devices in the field in which they are installed.

17. Method according to claims 1 to 15, wherein self-diagnostic commands are periodically carried out at points in time, in which the modules are not used for the assay processing in the device.

18. Method according to any of the preceding claims, wherein beyond the the sensors and actuators which are necessary and already integrated in the specific module for the main tasks thereof, additional dedicated sensors are provided to improve the precision of the diagnosis further.

19. Method according to any of the preceding claims, wherein actuators are chosen form the group including but limited to stepper motors, EC and DC motors, Peltier elements, RFID transmitters, Heaters, Valves, Fans, lifting magnets, and Pumps.

20. Method according to any of the preceding claims, wherein sensors are chosen from the group inculding, but not limited to photocells, Hall sensors, Temperature sensors, Acoustic sensors, Optical sensors, Sensors for measuring capacitances, pressures, flows, fill levels, Rotary Encoders, Linear Encoders, and RFID receivers.