Patent Description:
Conventionally, automated plants may utilize ultrasonic testing of components, such as connecting rods for engines, for example.

Such plant may comprise for example Connecting Rod (CR) Phased Array Ultrasonic Test (PAUT). The plant may be used to detect material discontinuities (Ultrasonic Test-UT indications) and avoids that parts with flaws, that might cause catastrophic engine failures, are installed on the field.

The inspection may be performed with the PAUT technique, which is for metal pieces equivalent to the echography test in medical field.

Ultrasonic inspection technique is a known way to inspect a piece, like a metal cast or forged piece, whether it comprises any faults. An ultrasonic probe is moved around the piece either manually or with an automated device in order to utilize high frequency sound waves penetrating through the piece. The sound waves propagate in the piece and part of the waves reflects from the surfaces of the piece and from the faults of the piece. The reflected waves can be detected and therefore used to detect the faults. The place, size and shape of the imperfection can be deduced from the reflected waves. It is also known to utilize more than one ultrasonic probe for the inspection.

The reflected waves are presented on a display of a device/system that is used for the inspection. An inspector checks the indications caused by imperfections on the display. In order to do that the inspector must have great professional skills and experience to make a proper analyse. If the indications are minor, the inspector can classify the piece to be accepted. If the piece comprises unacceptable imperfections or too many imperfections, the inspector should classify the piece as rejected as per criteria set forth.

The ultrasonic scanning is suitable for using with many metals. However, some metallic materials may have limitations to be checked by the ultrasonic scanning, like large grain size austenitic steels.

Patent application <CIT> discloses air-coupled ultrasonic inspection of rails.

Patent application <CIT> discloses a non-contact acoustic signal propagation property evaluation of synthetic fiber rope.

Eventually however, skills of the inspector or operator influence how good the inspection results are going to be. This can be problematic especially in cases where an inspector/operator does not have so much experience yet.

Thus, a solution is needed to enable accurate, easy-to-use, and reliable evaluation system that is configured to monitor and continuously evaluate collected data, and to monitor the overall material quality fluctuations. Moreover, the plant needs solutions to classify if the forged component is acceptable or not. Furthermore, more accurate levels of quality are needed to grade the forged component (e.g. how much is it good or not).

According to a first example aspect of the present invention, there is provided a computer implemented method for determining material quality of a forged component, the method comprising:.

In an embodiment, the method further comprises classifying the forged component to be accepted or rejected based on the quality information.

In an embodiment, the method further comprises combining the quality information with a selected subset of the historical data to provide anomalous data.

In an embodiment, the method further comprises determining anomality for at least one parameter in view of the quality ranges based on the anomalous data.

In an embodiment, the method further comprises providing the historical data as input to a neural network, wherein the neural network is comprised by the testing model.

In an embodiment, the method further comprises generating predicted data for a subset of the scan data as output by the neural network.

In an embodiment, generating the predicted data comprises reconstructing data of at least one probe or sensor, by the neural network, based on the scan data.

In an embodiment, generating the predicted data comprises determining correlation, by the neural network, between the data from the plurality of sensors.

In an embodiment, the method further comprises determining correlation, by the neural network, for the subset of the scan data among the historical data.

In an embodiment, the neural network is trained by means of signals from the individual probes or sensors for determining internal neural network parameters.

In an embodiment, the neural network is used for determination of the anomaly based on the error data.

In an embodiment, the neural network is configured to simulate the predicted data correlating with the history data, and the neural network is adjusted to the scan data by means of a training function.

According to the invention, the multiple parameters comprise at least two of following: Ultrasonic wave attenuation and transparency; Material surface quality; Material Cleanliness. In addition, the following parameters could also be evaluated: Ultrasonic indications; and Plant calibration.

In an embodiment, the multiple quality ranges comprise at least following:.

In an embodiment, the method further comprises adjusting at least one of the multiple quality ranges based on the reference data.

In an embodiment, the method further comprises calibrating sensitivity of the at least one ultrasonic probe based on the reference data.

In an embodiment, the testing model is arranged at a remote server apparatus.

In an embodiment, a plurality of ultrasonic probes or sensors to provide the scan data are operationally arranged to a manufacturing plant of an engine system.

In an embodiment, a subset of the plurality of probes or sensors comprises at least one probe or sensor and the historical data comprises data from the plurality of probes or sensors.

In an embodiment, the method further comprises receiving reference data from a plurality of remote apparatuses each comprising a plurality of reference probes or sensors to provide reference data.

In an embodiment, the reference data relate to different operational conditions of a reference engine.

In an embodiment, the reference data relate to operational and environmental measurement data of the reference engine.

In an embodiment, the method steps are arranged to be performed at a remote server apparatus.

According to a second example aspect of the present invention, there is provided a server apparatus for determining material quality of a forged component, comprising:.

According to a third example aspect of the present invention, there is provided a computer program embodied on a computer readable medium comprising computer executable program code, which code, when executed by at least one processor of an apparatus, causes the apparatus to:.

An example embodiment of the present invention and its potential advantages are understood by referring to <FIG> of the drawings. In this document, like reference signs denote like parts or steps.

<FIG> shows a schematic picture of a plant system <NUM> and a testing data system <NUM> according to an example embodiment.

The data system <NUM> comprises a control apparatus <NUM> configured to provide and operate a testing model (TM) <NUM> or at least part of the operations needed for the testing model (TM) <NUM>, such as providing sensor data or probe data data system <NUM> to a remote server apparatus <NUM> where the testing model (TM) <NUM> is configured to be maintained and operated.

When operating a testing data system <NUM> with a plurality of sensors or probes operationally connected to the data system <NUM>, data <NUM> is generated. Data <NUM> may be received from the plurality of sensors or probes operationally arranged to the plant system <NUM> to provide scan data <NUM>. The scan data <NUM> may be maintained within a data storage system. The data <NUM> may also comprise data from various data sources within the data system <NUM>, such as computer systems or communication systems, for example.

The dynamic testing model (TM) <NUM> may be maintained and operated by the control apparatus <NUM> and receive data <NUM> as input. Further inputs may be provided from various data sources, internal or external, as well as operational or environmental, as discussed throughout the description. Alternatively, the dynamic test model (TM) may be maintained and operated by a remote server apparatus <NUM> and receive input data <NUM> from the control apparatus <NUM>. In such scenario the TM <NUM> illustrated in <FIG> may be the plain data collection point or collection unit to provide the data to the remote server apparatus for the actual testing model (TM).

In an embodiment, there is provided a computer implemented method for determining material quality of a component <NUM>-<NUM>, the method comprising receiving ultrasonic scan data <NUM> for a plurality of scanned components <NUM>-<NUM>, maintaining the scan data <NUM> within a data storage system, determining historical data associated with multiple parameters based on the scan data <NUM> of the data storage system, generating a testing model (TM) <NUM> using the historical data, wherein the testing model (TM) <NUM> is configured to define multiple quality ranges for each parameter, scanning a component (CX) <NUM> using at least one ultrasonic probe (SCAN) <NUM> to provide component data (CD) <NUM>, and determining quality information of the component <NUM> using the testing model <NUM> and the component data <NUM>. The definition and determination of the multiple quality ranges may depend on the historical data associated with multiple parameters based on the scan data of the data storage system.

In an embodiment, the multiple quality ranges comprise at least following: a first range configured to indicate standard values; a second range configured to indicate expected deviation values; and a third range configured to indicate anomality deviation values.

In an embodiment, the component <NUM> may be classified to be accepted or rejected based on the quality information.

In an embodiment, the testing model <NUM> utilizes a neural network to predict signal behavior of equipment within the plant system <NUM> or component <NUM>-<NUM> processed therein with the goal of detecting abnormal behavior within the data system <NUM>, for example.

In an embodiment, by establishing an extended interface between the TM model <NUM> (or data collection unit in case the TM model is operated at remote server apparatus) and other systems like automation system <NUM>, and different types of components <NUM>-<NUM> within the plant <NUM>, for example, it is possible to gather vast amount of complex input data for TM <NUM> to process, learn, predict and detect anomalities.

Components <NUM>-<NUM> may comprise different components used at the plant <NUM> for assembling a system, such as an installed engine or a marine vessel, wherein the component is to be used, for example. Component (C1) <NUM> may comprise, for example, a Connecting Rod of an engine. By establishing the TM <NUM> (or data collection unit or point for the remote TM) for communicating between systems <NUM>-<NUM> it is possible to use artificial neural networks as computing systems. The neural network within the TM <NUM> itself may not be a single algorithm but a framework for many different machine learning algorithms to work together and process complex data inputs. Such systems learn to perform tasks by considering a lot of examples.

The neural network of the TM <NUM> may be configured to learn to reconstruct each data signal of the data <NUM> from an equipment of the system <NUM>, based on all other data signals of the data <NUM>.

Based on the difference between the reconstruction of a data signal and the actual signal, abnormal sensor behavior can be detected, as discussed in the description.

Uniqueness of the embodiments are based on the fact that, in general, the approach to anomaly detection is different. Essential feature is to use a neural network within the TM <NUM> to reconstruct (historical) data of the data <NUM> and compare it to actual data of the data <NUM> with the goal of detecting deviations (abnormal, anomalous behavior). This will be discussed more in relation to following figures and associated description.

In an embodiment, top priority for detection may be defined to be safety, and second and third priority can be set by the customer (surprising engine faults, wear, fuel consumption, etc.), for example. The TM <NUM> operates as a virtual expert for providing insight on anomalities.

The TM <NUM> solution will allow different levels of automation within the plant <NUM>. In first operation mode, TM <NUM> may be configured to provide an anomaly detection plan, which the engineers can use for scheduling their activities. In second operation mode, TM <NUM> may be configured to provide an embedded solution, wherein the sub-systems can notify the operator based on the anomaly detection plan, when to perform certain tasks or be switched on or set to standby. This notification may be repeated on the plant control room or remote-control station. In third operation mode, TM <NUM> may be configured to provide a solution to be fully automated and automatically executing the anomaly detection plan of the TM <NUM> with merely notification provided to the operator or remote-control station when performing different automated tasks.

Furthermore, characteristic information representing at least one operating characteristic of an installed engine or a marine vessel, where the engine or the component is to be installed and comprising the component CX <NUM>, may be received as data <NUM>. The dynamic TM <NUM> may use any available internal or external data, and the vessel related data including the characteristic information.

In an embodiment, the plant <NUM> collects, analyses and stores certain amount of scan data <NUM> (e.g. until 1Gb) for each inspected component <NUM>-<NUM>. That data may then be available on a dedicated server <NUM> and could be further processed.

In an embodiment, the TM model <NUM> is configured to control of connecting rods material quality. This TM model <NUM> is configured to classify material and gives the possibility to: define in objective and transparent way the quality of Connecting rod forgings (grade of the forging) through a newly defined innovative synthetic indicator; monitor and compare forgings quality trends within suppliers (statistical quality control); implement simple and direct visual tools to manage the inspection results; provide a powerful tool to support Supply Chain development and achieve cost optimization and scrap rates reduction for conrod forgings; and provide a tool for Risk Management and in developing new design by systematically identifying critical areas.

In an embodiment, data <NUM> collected over time of operation of the plant <NUM>, or any other plant or operating environment of the final product, such as an installed engine or marine vessel where the engine or the component is installed, can be analysed with data-mining techniques to identify a new set of parameters to grade in a robust and objective way the overall quality of the material. Quality may correspond to forging quality of the material, for example.

More specifically, the following parameters may be defined and evaluated:.

Each of the above parameters have their significance in defining the quality of the inspected part and embodiments enable to find appropriate ranges.

In an embodiment, for each parameter, three ranges may be defined that could be identified by different colours to simplify the presentation:.

Hence, the testing model TM <NUM> may utilize the above parameters. The following sections disclose further details for each of them, how they could be defined and how they are inferred based on experimental scan data <NUM>.

Technical effects provided by the embodiments provide a powerful tool to support supplier quality development, to reduce rate of scrap by improving the forging process quality directly at the supplier. Moreover it gives invaluable insight over the inner structure of the forgings, which can be used in the improved, more cost efficient design.

<FIG> presents an example block diagram of a control apparatus <NUM> (at a plant or at installation point such as marine vessel or a power plant, for example) in which various embodiments of the invention may be applied. The control apparatus <NUM> may comprise a user equipment (UE), user device or apparatus, such as a computer system,.

The general structure of the control apparatus <NUM> may comprise a user interface <NUM>, a communication interface <NUM>, a data storage <NUM>, capturing/sensor/probe device(s) <NUM> for scanning component or parts, a processor <NUM>, and a memory <NUM> coupled to the processor <NUM>. The apparatus <NUM> further comprises software <NUM> stored in the memory <NUM> and operable to be loaded into and executed in the processor <NUM>. The software <NUM> may comprise one or more software modules and can be in the form of a computer program product. The apparatus <NUM> may further comprise a user interface controller <NUM>.

In an embodiment, a plurality of ultrasonic probes or sensors <NUM> to provide the scan data may be operationally arranged to a manufacturing plant of an engine system, for example.

The processor <NUM> may be, e.g., a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a graphics processing unit, or the like. <FIG> shows one processor <NUM>, but the apparatus <NUM> may comprise a plurality of processors.

The memory <NUM> may be for example a non-volatile or a volatile memory, such as a read-only memory (ROM), a programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), a random-access memory (RAM), a flash memory, a data disk, an optical storage, a magnetic storage, a smart card, or the like. The apparatus <NUM> may comprise a plurality of memories. The memory <NUM> may be constructed as a part of the apparatus <NUM> or it may be inserted into a slot, port, or the like of the apparatus <NUM> by a user. The memory <NUM> may serve the sole purpose of storing data, or it may be constructed as a part of an apparatus serving other purposes, such as processing data. A proprietary test application (client application) <NUM> comprising the test model TM <NUM> (or TM data collection point in case the TM is arranged remotely at server apparatus) is stored at the memory <NUM>. Component data, sensor data and environmental data may also be stored to the memory <NUM> or to the data storage <NUM>. The program code <NUM> may comprise the dynamic test model (TM) <NUM> and the proprietary application <NUM> may comprise a client application for the TM, for example.

The user interface controller <NUM> may comprise circuitry for receiving input from a user of the apparatus <NUM>, e.g., via a keyboard, graphical user interface shown on the display of the user interfaces <NUM> of the control apparatus <NUM>, speech recognition circuitry, or an accessory device, such as a headset, and for providing output to the user via, e.g., a graphical user interface or a loudspeaker.

The communication interface module <NUM> implements at least part of data transmission. The communication interface module <NUM> may comprise, e.g., a wireless or a wired interface module. The wireless interface may comprise such as a WLAN, Bluetooth, infrared (IR), radio frequency identification (RF ID), GSM/GPRS, CDMA, WCDMA, LTE (Long Term Evolution), or <NUM> radio module. The wired interface may comprise such as universal serial bus (USB) or National Marine Electronics Association (NMEA) <NUM>/<NUM> standard for example. The communication interface module <NUM> may be integrated into the apparatus <NUM>, or into an adapter, card or the like that may be inserted into a suitable slot or port of the apparatus <NUM>. The communication interface module <NUM> may support one radio interface technology or a plurality of technologies. The apparatus <NUM> may comprise a plurality of communication interface modules <NUM>.

A skilled person appreciates that in addition to the elements shown in <FIG>, the apparatus <NUM> may comprise other elements, such as microphones, extra displays, as well as additional circuitry such as input/output (I/O) circuitry, memory chips, application-specific integrated circuits (ASIC), processing circuitry for specific purposes such as source coding/decoding circuitry, channel coding/decoding circuitry, ciphering/deciphering circuitry, and the like. Additionally, the apparatus <NUM> may comprise a disposable or rechargeable battery (not shown) for powering when external power if external power supply is not available.

In an embodiment, the apparatus <NUM> comprises speech recognition means. Using these means, a pre-defined phrase may be recognized from the speech and translated into control information for the apparatus <NUM>, for example.

The data storage <NUM> and the scan probes or sensor device(s) <NUM> may be configured to be comprised by the apparatus <NUM> or connected as separate devices to the apparatus <NUM>. In case they are comprised in the apparatus <NUM> they may be connected to the apparatus <NUM> using an internal bus of the apparatus <NUM>. In case they are external devices connected to the apparatus <NUM> they may be connected to the apparatus <NUM> using communication interface <NUM> of the apparatus <NUM> or using the internal bus.

In an embodiment, an ultrasonic test system is composed of an ultrasonic scan device <NUM> and a control apparatus <NUM>. The scan device <NUM> may be part of the apparatus <NUM> or separately connected to the apparatus <NUM>. The ultrasonic scan device <NUM> may be a transceiver configured for receiving reflected ultrasound acoustic pressure which emanate from a scanned component <NUM> as a result of an interaction between the component <NUM> and emitted ultrasound energy <NUM>. When the emitted ultrasound acoustic pressure <NUM> collides with or otherwise interacts with the component <NUM>, some of the emitted ultrasound acoustic pressure <NUM> is reflected from the component <NUM>. In some embodiments, the ultrasonic transceiver <NUM> is also configured for emitting the emitted ultrasound acoustic pressure <NUM> into the component <NUM>. For example, the ultrasonic transceiver <NUM> includes both an ultrasonic transmitter and an ultrasonic receiver.

In some embodiments, a liquid interface, sometimes called a "couplant", is used to improve ultrasonic transmission between the ultrasonic transceiver <NUM> and the component <NUM>. The liquid interface can be any suitable substance. In other embodiments, the ultrasonic test system is implemented as an immersion system, wherein the component <NUM> and part or all of the ultrasonic transceiver <NUM> are submerged in a basin of water or another ultrasound-conductive medium. Still other ultrasound techniques are considered, including phased-array probes, and the like.

In some embodiments, the ultrasonic transceiver <NUM> has one or more variable settings. Depending on one or more parameters of the component <NUM>, the variable settings of the ultrasonic transceiver <NUM> are adjusted. For example, a frequency of the emitted ultrasound energy is adjusted based on the materials which make up the component <NUM>. The frequency of the emitted ultrasound energy can be any suitable frequency, for example a normalized frequency between <NUM> and <NUM>, as appropriate. In another example, the strength of the emitted ultrasound energy is adjusted based on the thickness of the component <NUM>. Still other aspects of the emitted ultrasound energy, including amplitude, phase, and the like, are adjustable as appropriate.

The control apparatus <NUM> is communicatively coupled to the ultrasonic transceiver <NUM> for obtaining therefrom ultrasound data representative of the emitted and/or reflected ultrasound energy and optionally various information pertaining to the emitted ultrasound energy. For example, the control apparatus <NUM> receives reflected ultrasound data (e. g scan data) which is representative of the reflected ultrasound energy. In some embodiments, the control apparatus <NUM> receives one or more digital representations of the reflected ultrasound energy and optionally of the emitted ultrasound energy. In other embodiments, the control apparatus <NUM> receives information which characterizes the reflected ultrasound acoustic pressure and optionally the emitted ultrasound energy. The control apparatus <NUM> is also configured for determining material quality of the component. Alternatively, mould or cast quality may be determined, as well as determining material quality or whether kiss bonds are present in the component <NUM> based on the reflected ultrasound energy, and optionally the emitted ultrasound energy.

In an embodiment, a proprietary test application (client application) <NUM> is maintained at the apparatus <NUM>. The application <NUM> may comprise the TM model <NUM> or the TM model related data, such as component detection data that is received from the server apparatus where the TM model is run, for example.

In an embodiment, a reference piece <NUM> may be scanned utilizing at least one ultrasonic probe <NUM> to collect at least one reference data set from said scanning of the reference piece. Based on the reference data set the system sensitivity may be calibrated with the reference piece data. Furthermore it is possible to analyse the collected data of the scanned reference piece, exporting the A-scan core data of interest (minimum, maximum, average signal amplitude, size and number of areas with relevant signal amplitudes) and classifying the component to be accepted or rejected utilizing said indication data when comparing it to set acceptability level.

In an embodiment, at least one of the multiple quality ranges may be automatically adjusted based on the reference data. It is also possible to calibrate sensitivity of the at least one ultrasonic probe based on the reference data.

<FIG> shows a schematic drawing of reflected ultrasound data 200a for a component <NUM> and illustration 200b of the scan device <NUM>, the component <NUM> and ultrasound acoustic pressure <NUM> according to an example embodiment.

A probe (such as scan device <NUM> in <FIG>) sends a ultrasonic sound wave, an initial pulse (IPE), into a tested component <NUM>. A front wall echo (FWE) is due to reflection from the front surface of the component <NUM>. There are multiple indications from back wall echoes BWE1-BWE4 of the component <NUM> as shown. BWE1 may be first reflection from the first back wall surface, parallel to the front surface of the component <NUM>, and BWE2 may be due to the second order rear surface reflection of the component. BWE3-<NUM> may be due to third and fourth order rear surface reflections from the back wall surface. A defect within the component material and/or an anomalous material structure may influence or adjust the ultrasound beam attenuation. That may be illustrated, for example, by damping level with a consequential increase in the reduction rate of consecutive BEW's. The sound path corresponds to the signal path of the ultrasonic sound within the component material.

In an embodiment, reflected ultrasound data 200a may illustrate an initial probe pulse IPE, a front wall echo FWE and back wall echoes BWE1-<NUM>. The front wall echo FWE and back wall echoes BWE1-BWE4 are produced when the emitted ultrasound acoustic pressure reflects off of the front surface (FWE) and rear surface (BWE1-<NUM>), respectively, of the component <NUM>.

Formulas for calculation of the attenuation data are based on the scan data <NUM> (see <FIG> or <FIG>, for example). When the component geometry of the scanned component changes, then also the formulas change. In general the formula may be expressed as: <MAT>.

<FIG> shows a schematic picture of an testing model (TM) <NUM> and related information flows according to an example embodiment.

Elements <NUM>-<NUM> may have alternative ways to connect with each other and <FIG> only shows one example embodiment. Furthermore, only connections that may relate somehow to testing (TM) <NUM> are illustrated.

The testing model (TM) <NUM> can be configured to operate as a stand-alone solution or as an integrated part of the data management system of the plant. The testing model (TM) <NUM> enables automation and further enables a higher degree of autonomous operation on plant and paves the way for data management for autonomous plant data system.

In an embodiment, the testing model (TM) <NUM> is interfaced with systems like automation system, scanner/probe/sensor device, user interface, scan data of data storage, as shown in <FIG>, for example. The testing model (TM) <NUM> may further be configured to receive and manage information about the health status of sub-systems directly or through automation systems. The testing model (TM) <NUM> can generate tasks and/or instructions for the automation based on input data, such as scan data.

The testing model (TM) <NUM> may further be arranged to receive environmental information <NUM>. The environmental information <NUM> may represent at least one environmental characteristic of the installation where the part or component is about to be used eventually.

In an embodiment, the testing model (TM) <NUM> may generate at least one task for controlling an automation element of the automation system <NUM> of the plant automatically based on the testing model (TM) <NUM> and control the associated automation element of the plant automation system <NUM> based on the determined task.

In an embodiment, the automation element <NUM> of the plant system is configured to control at least one of the following: scanner/probe device <NUM>, component movement tool that selects and transferred the scanned component to scanning position, or component robot that moves the component to be scanned in scan position, for example.

In an embodiment, a control apparatus <NUM>, (see e.g. <FIG>) processing the testing model (TM) <NUM> is configured to receive confirmation of the task being performed from an automation element <NUM> being controlled by the task, and to update the testing model (TM) <NUM> in response to the received confirmation.

In an embodiment, the testing model (TM) <NUM> may generate automation plan (AP) <NUM> and utilize the automation plan (AP) <NUM> for determining control tasks within the plant system automatically based on the testing model (TM) <NUM>.

Because the testing model (TM) <NUM> has access to information about optimal operation conditions of the sub-systems, the model can help to avoid stressing engines, generators and other subsystems, as the safety limit parameters are known to the testing model (TM) <NUM>. An operating mode may be used wherein only confirmed request from the operator is needed, and the testing model (TM) <NUM> may allow running sub-systems outside the optimal operation conditions.

The automation plan <NUM> information can be provided in a first mode as a schedule made available to the engineers to follow. The engineers may perform the scheduled tasks for the automation system <NUM> based on the plan <NUM>. In a second mode, the automation plan <NUM> may be embedded in the main display of the plant control room, for example. The automation system may be further configured to provide an integrated guidance tool to prompt the operator when a task should take place and by acknowledgement from the operator enable and perform the task and end the task when performed. A third mode allows a fully automated solution, where the operator may only be informed about the automation plan <NUM> or the tasks determined by the testing model (TM) <NUM>. Optionally, current status of the model and next steps may be informed to the operator but the testing model (TM) <NUM> is configured to control automation elements automatically. In such embodiment the automation plan <NUM> may be optional.

It is possible to override the testing model (TM) <NUM> by changing it to standby mode and allowing a manual operation of the automation systems and the sub-systems. At the third mode, the testing model (TM) <NUM> can operate autonomously together with all the sub-systems. Instead of notifying the operator, the testing model (TM) <NUM> may log (e.g. using the automation plan <NUM>) the activities and events and will only request assistance from the mission controller or a human operator in case the testing model (TM) <NUM> is facing a situation it cannot handle or it is not available for operation.

In an embodiment, the automation plan <NUM> may also comprise automatic information being sent to supplier data system, to next port where vessel is about to visit, or to operator of the installed engine or the marine vessel comprising corresponding component(s), for example. The information being sent may relate to, for example, estimate of services needed. By doing that the harbor authorities can make a better estimate the spare parts and labor for the vessel about to be docked.

In an embodiment, the testing model (TM) <NUM> is configured to receive input from an operator (USR) <NUM> either at plant or remote at a vessel or a ground station, for example. In certain pre-defined operating modes or tasks, it may be required that operator acknowledgement is received from the operator (USR) <NUM> for the detected anomaly, for adjusting other inputs for the model, for determined task the testing model (TM) <NUM> before controlling an automation element of the plant based on the determined task in response to the received operator acknowledgement. The user input <NUM> may be provided by an insight application.

In an embodiment, the testing model (TM) <NUM> may be updated in real-time based on the scan data <NUM>, other characteristic information <NUM> and user input <NUM>, for example.

In an embodiment, the testing model (TM) <NUM> is configured to be generated using the historical data <NUM>-<NUM>, wherein the testing model <NUM> is configured to define multiple quality ranges <NUM> for each parameter. The historical data associated with multiple parameters may be determined based on the scan data <NUM> of the data storage system.

According to the invention, the multiple parameters comprise at least two of following: Ultrasonic wave attenuation and transparency; Forging surface quality; Material Cleanliness. In addition, the following parameters could also be evaluated: Ultrasonic indications; and Plant calibration.

Ultrasonic testing (UT) material transparency may be evaluated through the measurement of the material ultrasonic (US) attenuation coefficient. This value is obtained by three ultrasonic US frequency probes or eventual signal filter ranges applied to a signal of frequency X MHz (e.g. <NUM> probe) setup (scanning device <NUM>), for example:.

The lower is the attenuation(s), the higher is the material transparency at the considered US frequency. Higher attenuation values to statistical mean values is related to coarser grain structures and/or presence of discontinuities. Low attenuation values are ascribed to fine structure materials with improved mechanical properties.

Formulas for calculation of the attenuation data are based on the scan data <NUM>. When the component geometry of the scanned component changes, then also the formulas change. In general the formula may be expressed as: <MAT>.

The following example formulas for a dedicated type of component type for the calculation of the Attenuation data are based on the scan data <NUM>. <MAT> <MAT> <MAT>.

The obtained attenuation data are having the unit of [dB/m] and the obtained values may be evaluated with the assignment of the following standard, where:.

For example, a standard for forged connecting rods is as per following table:.

In particular, components or parts classified as 'Anomalies' may be further investigated, since that could be symptomatic of poor raw material quality, production process faults and potential failure risk on field.

<FIG> illustrates an example view on attenuation data based on scan data <NUM>, according to an example embodiment of the invention.

In an embodiment, considering the plot of attenuation data (collected from about <NUM> pcs) in <FIG>, following samples have shown the highest attenuation deviations and have been classified as 'Anomalies'. It is visible that deviations (circles) occur in group of close forging number: homogeneous forging, pre- and post-forging heating as well as probably same quenching and tempering batch.

In particular the main factor considered to be influencing the material attenuation are the material micro/macro structure and material cleanliness (presence of inhomogeneity in the material). Both above material characteristics can affect the component fatigue resistance and lifetime.

In order to further validate the attenuation `Anomalies model' it is possible to further process the properties of samples having anomalies in the attenuation data. Furthermore, it is possible to collect parts failed on field attenuation data in order to understand if this parameter was having any role in such failures.

Ultrasonic testing (UT) of surface quality is evaluated considering the data corresponding to a component, such as a Connecting rod lower part (CRLP) as forged (arch) external surface reflection (from the component rear side). In particular the considered signal reflection is based on the two shear waves scan performed from CRLP internal diameter with an incident angle within the range of <NUM>°-<NUM>°, for example of <NUM>°, to the radial plane.

In an embodiment, main data of interest, for example for forged components, are:.

When a sample is classified as deviation, then a visual inspection and other non-destructive testing (NDT) may be performed. When 'Anomalies' are observed then the part may be tested with visual and magnetic particles inspection; as well as grinding for discontinuity removal could be necessary.

Considering the forging surface the range standard is defined as per following, where:.

Standard (range <NUM>) corresponds to standard values;
Expected deviations (range <NUM>) corresponds to expected deviations;
Anomalies (range <NUM>) corresponds to severe anomalies.

With this surface quality parameter it is possible for the automated plant to evaluate also the quality of the raw, e.g. forged, non-machined surfaces. In particular, considering the mean and maximum reflection values from the forging surface, it is possible to claim and improve with suppliers the presence of: Superficial forging laps; excessively deep grinding marks; and highly rough as forged surfaces, for example.

Target is to create a statistical base considering the indications observed during each scan. The obtained values are compared with the overall statistical base, comparing it within batches and suppliers. This data gives the understanding: If raw bar before forging was a good or poor quality material, and if the part could have issues with the fatigue resistance.

UT cleanliness may be evaluated considering the data corresponding to the inspection scan perpendicular to the parts forging grain flow.

In an embodiment, main data of interest are:.

a) Average signal amplitude which is related to the mean cleanliness. This data are influenced by presence of discontinuities in the material, as micro-inclusions and material macrostructure. b) Max signal amplitude is influenced by the presence of macro discontinuities as bigger inclusions could be, or planar defects (cavities or internal tears, or hydrogen flakes). c) The parameter over reference is a count of unit data that exceeded different set of the threshold amplitude. It is a count of the size and quantity (Fault areas) of indications. Considering <NUM>% FSH (full screen height) as the reference threshold value, indications data having amplitudes greater than ½ (<NUM>% FSH) and ¼ (<NUM>% FSH) of it are additionally considered and count. d) Cleanliness K is defined as number of detected indications at reference amplitudes divided by the scanned (control) volume (in cm<NUM>).

US (ultrasonic) indications is the standard evaluation parameter which is conventionally used for Ultrasonic inspection evaluation of indications. The following standard is applied in that case.

In an embodiment, monitoring of the automated plant calibration is performed by the scan at each US setup loading of the 'Check block' which has known reflectors. Collected reflector data are compared to standard reflector amplitudes and evaluated if any calibration of the plant is needed. Typically deviations in calibration data might occur due to seasonal oscillations of day mean temperature.

The following standard is applied in such case.

In an embodiment, the plant <NUM> may be equipped with reference reflectors that are scanned for each applied scan plan. Such data are collected and monitored in order to assure proper plant and setups calibration conditions. The check block reflectors of each scan are compared with the standard calibration data. Such check evidences the variation of the setups calibration due to temperature continuous variations, especially in summer time. Such deviations can be improved as consequence of the implemented temperature conditioning system of the inspection cell. The check also provides evidence of any US probe performance deterioration or system geometry offsetting.

<FIG> shows a schematic picture of a computer implemented method for determining material quality of a component <NUM>, <NUM> and a testing model (TM) <NUM> according to an example embodiment. The testing model (TM) <NUM> may be configured to be maintained at a remote server apparatus, at a remote control apparatus, at a plant testing apparatus, at an installed engine apparatus, a marine vessel control apparatus or any combination of those. For example, data collection <NUM> may be arranged from one apparatus (e.g. ultrasonic testing probe, scanning device), an insight input <NUM> from a second apparatus (e.g. remote control apparatus) and the actual data processing and neural network involvement at a third apparatus (e.g. remote server apparatus), for example.

First, ultrasonic scan data is provided for a plurality of scanned components <NUM>, <NUM> using at least one ultrasonic probe or scan device <NUM> operationally arranged at a plant or other remote installation place like a marine vessel (or power plant), or from other data sources within the data system, for example. For example, a plurality of sensors may be operationally arranged to an engine system of the marine vessel or a power plant.

Received scan data is maintained within a data storage system <NUM>. The data storage system <NUM> may be arranged to a remote server apparatus, such as cloud server, but may also be at least temporarily stored at plant data storage. The maintained scan data of the data storage system <NUM> is provided as source data for further processing.

Second, historical data <NUM> is determined based on the scan data of the data storage system <NUM>. The historical data <NUM> is associated with multiple parameters based on the scan data of the data storage system. The historical data <NUM> may comprise data from a plurality of probes <NUM> and relating to a plurality of different components.

Third, a testing model (TM) <NUM> is generated using the historical data <NUM>, wherein the testing model <NUM> is configured to define multiple quality ranges for each parameter.

Historical data <NUM> may comprise data from a plurality of scans and components.

Parallel to the dynamic update of the testing model <NUM>, component data <NUM> may be provided by scanning a component <NUM> using at least one ultrasonic probe <NUM>. The component data <NUM> may also be first maintained in the data storage system <NUM> and forwarded over connection <NUM>. For sake of simplicity for <FIG>, only the connection <NUM> is shown, no matter the component data may also originate directly from the scanning device or probe <NUM>.

Fourth, quality information <NUM> of the component is determined using the testing model <NUM> and the component data <NUM>.

In an embodiment, further steps may be provided.

For example, fifth, the quality information <NUM> may be further processed by data processing <NUM> by combining the quality information <NUM> with a selected subset <NUM> of the historical data <NUM>.

Sixth, anomalous data <NUM> is detected based on the data processing <NUM>. The testing model <NUM> and related processing <NUM>, <NUM>, <NUM>, and combining with the history data <NUM>, may for example result to a peak (see e.g. <FIG>) in certain actual scan data that is not as predicted by the model <NUM>. Such deviation may indicate an anomaly even though the peak may be not high compared to the expected scan probe behavior. Alternatively, values in certain scan data may be much higher than usual, but the model <NUM> has predicted such behavior based on the other probe/sensor signals. Therefore, such data may not be anomalous.

In an embodiment, anomality may be detected for at least one parameter in view of the quality ranges based on the anomalous data <NUM>. Depending on the component <NUM> and its installation related requirements certain parameters and quality ranges may be emphasized when determining anomality.

Detected anomality may trigger certain tasks within the plant data system, either automatically, or in response to some confirmation by operating user, for example.

In an embodiment, a continuous evaluation application <NUM> for evaluating anomality results may be provided. The evaluation application <NUM> may receive user input by experts via online application, for example. The results <NUM> may be labeled in the form of an 'indication of unusual data'. A tool of the application <NUM> used may be called an Anomaly Timeline, and it may show heat bars per probe, per component, and/or per installation. The redder a heat bar is, the more unusual the data behavior is at that point in time.

The experts may use the Anomaly Timeline tool to find the 'needles in the haystack'. The experts may review all the periods of data indicated by the model <NUM> as unusual in step <NUM> and verify these. The expert users may confirm if it is really something that should be reported to the customer, for example. Similarly, they decide if the anomaly <NUM> is a 'false positive' no matter the model <NUM> marked some period as unusual while the expert thinks it is as expected.

Once the engine expert is convinced that data is unexpected, he/she may create a case about it in communication channel, such as Case Collaboration Chat. Here the data can be discussed with all stakeholders; experts, installation crew, etc. Together the data can be studied, and a solution can be found to tackle the problem and make the data return to expected behavior.

Once the problem is solved, the case can be closed. While doing this, the stakeholders can indicate what the end solution was that solved the problem, what the severity of the case was, and more case related information.

Both the first validation round (the Anomaly Timeline) as the second (the Case Collaboration Chat) provide valuable feedback on the output <NUM> of the model <NUM>. The model itself can be trained better if it knows what anomalies are considered relevant (with a high severity), and what output of the model is falsely positive. The output of the model can be processed more automatically if it can be taught to know what approach often solves unexpected data behavior.

The feedback information <NUM> may be gathered by the evaluation application <NUM> continuously. If the expert marks a period of data as irrelevant in the Anomaly Timeline, it may be configured to disappear at the view of all other experts, so nobody must check that period of data anymore. Then again, if a stakeholder of a case closes the cases and marks the case as highly severe, a separate model may learn this so that future similar anomalies will be more prominently indicated in the Anomaly Timeline. This will lead to the engine expert validating the data earlier, thereby shortening the time between model output and case creation. This would benefit the stakeholders as this enables information to be available faster to them.

<FIG> shows another schematic picture of a computer implemented method for determining forging quality of a component, and system of a testing model (TM) <NUM> for according to an example embodiment. The testing model (TM) <NUM> may be configured to be maintained at a remote server apparatus, at a remote control apparatus, at a plant control apparatus, at an installed engine apparatus, at a marine vessel control apparatus or any combination of those. For example, data collection <NUM> may be arranged from one apparatus (e.g. plant testing apparatus with scanning probes), an insight input <NUM> from a second apparatus (e.g. remote control apparatus) and the actual data processing and neural network involvement at a third apparatus (e.g. remote server apparatus), for example.

First, ultrasonic scan data is received for a plurality of scanned components <NUM>, <NUM>, from various US scanning probes or sensors <NUM>, for example.

The scanning probes or sensors <NUM> may be operationally arranged to the plant, or to marine vessel (or power plant), or scan data may be received from other data sources within the data system, for example. For example, a plurality of probes or sensors may be operationally arranged to an engine system of the marine vessel or a power plant.

Received data is maintained as scan data within a data storage system <NUM>. The data storage system <NUM> may be arranged to a remote server apparatus, such as cloud server, but may also be at least temporarily stored at plant data storage. The maintained scan data of the data storage system <NUM> is provided as source data for further processing.

Second, historical data <NUM> is determined based on the scan data of the data storage system <NUM>. The historical data <NUM> is associated with multiple parameters based on the scan data of the data storage system <NUM>.

The historical data <NUM> may comprise data from a plurality of probes and sensors and for a plurality of components.

A testing model <NUM> is generated using the historical data <NUM>, wherein the testing model is configured to define multiple quality ranges for each parameter, and the testing model <NUM> comprises a neural network <NUM>. The historical data <NUM> may be provided as input to the neural network <NUM>.

Third, the neural network <NUM> generates predicted data <NUM> for a subset of the scan data as output by the neural network <NUM>. The subset comprises at least one parameter of the plurality of parameters, and may relate to a certain type of component, for example. Historical data <NUM> may comprise data from a plurality of probes for a plurality of components <NUM>, <NUM> in view of different parameters. Parallel to the generation of predicted data <NUM>, actual data <NUM> is determined <NUM> from the probe data of the data storage system <NUM> based on association to the subset of the scan data. The actual data <NUM> may comprise component data scanned by the probe for the component <NUM>.

In an embodiment, the subset of the scan data may comprise scan data from several components <NUM> but not necessarily the total number of components wherefrom scan data is received. For example, scan data is received from N components.

In an embodiment, the testing model (TM) <NUM> is configured to generate reconstruction data <NUM> for a period of time, for component C1, based on all other scan data <NUM> in that same period, C2. Cn using the neural network <NUM>. This is done for each component C1. If reconstruction data <NUM> is made for component Cx, that reconstruction data <NUM> is compared to the actual data <NUM> of Cx in the same period of time. Similar comparison can then be done for each component C1. Cn and for each parameter.

Fourth, the predicted data <NUM> is combined with the actual data <NUM> for the subset of the plurality of sensors to provide error data <NUM> that may equal to the quality information. The actual data <NUM> is determined from the scan data of the data storage <NUM> system based on association to the subset of the scan data.

Fifth, the error data <NUM> is processed by error data processing <NUM> of the testing model <NUM> by combining the error data <NUM> with a selected subset <NUM> of the historical data <NUM>.

In an embodiment, a subset <NUM> of the plurality of probes or sensors comprises at least one probe or sensor and the historical data <NUM> comprises data from the plurality of probes or sensors.

Sixth, anomalous data <NUM> is detected based on the error data processing <NUM>. The testing model <NUM> and related processing <NUM>, <NUM>, <NUM>, and combining with the history data <NUM> of scan data, may for example result to a peak in certain actual component scan data that is not as predicted by the model <NUM> as predicted data <NUM>. Such deviation may indicate an anomaly even though the peak may be not high compared to the expected component behavior. Alternatively, values in certain component parameter may be much higher than usual, but the model has predicted such behavior based on the other component scan signals. Therefore, such data may not be anomalous.

In an embodiment, the neural network <NUM> is used for determination of an anomaly <NUM> based on the error data <NUM>.

In an embodiment, the neural network <NUM> is configured to simulate the predicted data <NUM> correlating with the history data <NUM>, and the neural network <NUM> is adjusted to the scan data <NUM> by means of a training function. The training function may comprise at least one of the following: Gradient descent function; Newton's method function; Conjugate gradient function; Quasi-Newton method function; and Levenberg-Marquardt function.

In an embodiment, generating the predicted data <NUM> comprises reconstructing data of at least one probe or sensor <NUM>, by the neural network <NUM>, based on the scan data stored as part of the history data <NUM>. Generating the predicted data <NUM> comprises determining correlation, by the neural network <NUM>, between the data from the plurality of sensors. Correlation may be determined, by the neural network, for the subset of the scan data among the historical data. The neural network may be trained by means of signals from the individual probes or sensors for determining internal neural network parameters.

In an embodiment, the neural network is used for determination of the anomaly based on the error data. The neural network <NUM> may also be configured to simulate the predicted data <NUM> correlating with the history data <NUM>, and the neural network <NUM> is adjusted to the scan data by means of a training function.

Detected anomality may trigger certain tasks within the system, either automatically, or in response to some confirmation by operating user, for example.

In an embodiment, a continuous evaluation application <NUM> for evaluating the neural network based anomality results may be provided. The evaluation application <NUM> may receive user input by experts via online application, for example. The results <NUM> of the neural network process <NUM>-<NUM> may be labeled in the form of an 'indication of unusual data'. A tool of the application <NUM> used may be called an Anomaly Timeline, and it may show heat bars per sensor, per asset, and/or per installation. The redder a heat bar is, the more unusual the data behavior is at that point in time.

Both the first validation round (the Anomaly Timeline) as the second (the Case Collaboration Chat) provide valuable feedback on the output <NUM> of the model (TM) <NUM>. The model itself can be trained better if it knows what anomalies are considered relevant (with a high severity), and what output of the model is falsely positive. The output of the model can be processed more automatically if it can be taught to know what approach often solves unexpected data behavior.

In an embodiment, the neural network <NUM> is trained by means of signals from the individual scanned components <NUM> for determining internal neural network parameters.

The feedback information may be gathered by the evaluation application <NUM> continuously. If the expert marks a period of data as irrelevant in the Anomaly Timeline, it may be configured to disappear at the view of all other experts, so nobody must check that period of data anymore. Then again, if a stakeholder of a case closes the cases and marks the case as highly severe, a separate model may learn this so that future similar anomalies will be more prominently indicated in the Anomaly Timeline. This will lead to the expert validating the data earlier, thereby shortening the time between model output and case creation. This would benefit the stakeholders as this enables information to be available faster to them.

If a stakeholder of a case closes the case and indicates the solution that solved the unexpected behavior, the Case Collaboration Chat can, next time the behavior occurs, come up with an automatic proposed solution. This greatly improves the time to solve a case. This learning will be utilizing pattern recognition techniques on the related data of all cases created on all engines, for example. This will enable knowledge from one single expert to become available to all experts.

<FIG> presents an example block diagram of a server apparatus <NUM> in which various embodiments of the invention may be applied.

The general structure of the server apparatus <NUM> comprises a processor <NUM>, and a memory <NUM> coupled to the processor <NUM>. The server apparatus <NUM> further comprises software <NUM> stored in the memory <NUM> and operable to be loaded into and executed in the processor <NUM>. The software <NUM> may comprise one or more software modules, such as service application <NUM> and can be in the form of a computer program product. The software <NUM> may comprise the testing model (TM) <NUM>, <NUM>, <NUM> and the service application <NUM> may be configured to communicate with the client application <NUM> (see <FIG>) arranged at control apparatus <NUM>, for example. The client application <NUM> may be configured to provide the scan data to the testing model <NUM>, <NUM>, <NUM>, for example.

The processor <NUM> may be, e.g., a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a graphics processing unit, or the like. <FIG> shows one processor <NUM>, but the server apparatus <NUM> may comprise a plurality of processors.

The memory <NUM> may be for example a non-volatile or a volatile memory, such as a read-only memory (ROM), a programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), a random-access memory (RAM), a flash memory, a data disk, an optical storage, a magnetic storage, a smart card, or the like. The server apparatus <NUM> may comprise a plurality of memories. The memory <NUM> may be constructed as a part of the server apparatus <NUM> or it may be inserted into a slot, port, or the like of the server apparatus <NUM> by a user. The memory <NUM> may serve the sole purpose of storing data, or it may be constructed as a part of an apparatus serving other purposes, such as processing data.

The communication interface module <NUM> implements at least part of radio transmission. The communication interface module <NUM> may comprise, e.g., a wireless or a wired interface module. The wireless interface may comprise such as a WLAN, Bluetooth, infrared (IR), radio frequency identification (RF ID), GSM/GPRS, CDMA, WCDMA, LTE (Long Term Evolution), or <NUM> radio module. The wired interface may comprise such as universal serial bus (USB) or National Marine Electronics Association (NMEA) <NUM>/<NUM> standard for example. The communication interface module <NUM> may be integrated into the server apparatus <NUM>, or into an adapter, card or the like that may be inserted into a suitable slot or port of the server apparatus <NUM>. The communication interface module <NUM> may support one radio interface technology or a plurality of technologies. Scan data, component data, parameter data, ranges related data, and data associated with environmental data of the end product, such as engine of a marine vessel (or power plant), as well as measured engine parameters relating to operation conditions of the engine may be received by the server apparatus <NUM> using the communication interface <NUM>.

The e-mail server process <NUM>, which receives e-mail messages sent from control apparatuses <NUM>, and remote computer apparatuses via the network <NUM>. The server <NUM> may comprise a content analyzer module <NUM>, which checks if the content of the received message meets the criteria that are set for new activity data item of the service. The content analyzer module <NUM> may for example check whether the e-mail message contains a valid activity data item to be used as reference data item. The valid reference data item received by the e-mail server is then sent to an application server <NUM>, which provides application services e.g. relating to the user accounts stored in a user database <NUM> and content of the content management service. Content provided by the service system is stored in a content database <NUM>.

A skilled person appreciates that in addition to the elements shown in <FIG>, the server apparatus <NUM> may comprise other elements, such as microphones, displays, as well as additional circuitry such as input/output (I/O) circuitry, memory chips, application-specific integrated circuits (ASIC), processing circuitry for specific purposes such as source coding/decoding circuitry, channel coding/decoding circuitry, ciphering/deciphering circuitry, and the like. Not all elements disclosed in <FIG> are mandatory for all embodiments.

According to an embodiment, the server apparatus <NUM> may be configured to receive ultrasonic scan data for a plurality of scanned components; maintain the scan data within a data storage system; determine historical data associated with multiple parameters based on the scan data of the data storage system; generate a testing model using the historical data, wherein the testing model is configured to define multiple quality ranges for each parameter scan a component using at least one ultrasonic probe to provide component data; and determine quality information of the component using the testing model and the component data.

<FIG> shows an example view on a system, wherein material quality of a component may be determined according to an example embodiment of the invention.

In an embodiment, a plant system <NUM> may comprise any local site where a component <NUM> is manufactured, installed to some product (like an engine <NUM>, for example) or inspected for quality in general.

In an embodiment, local data processing <NUM> at the plant system <NUM> may comprise at least following steps and items including a computer implemented method for determining material quality of a component. The method may comprise receiving plant, device or component related information, such as component or material data <NUM>, and ultrasonic scan data <NUM> for a plurality of scanned components, for example. The scan data <NUM> may be maintained within a data storage system at the plant system <NUM>. Data <NUM> may also comprise raw data.

In an embodiment, historical data associated with multiple parameters may be determined based on the scan data <NUM> of the data storage system at the plant site <NUM>, and generating a testing model using the historical data, wherein the testing model is configured to define multiple quality ranges for each parameter. Furthermore, a component is scanned using at least one ultrasonic probe to provide component data, and determining quality information of the component using the testing model and the component data.

In an embodiment, component or material data <NUM> or scan data <NUM> may be transmitted <NUM> from the local data processing <NUM> of the plant system <NUM> to a cloud data processing <NUM>. Furthermore, the testing model <NUM> may be generated and/or maintained in either or both of the processing entities <NUM>, <NUM>, as shown in <FIG>.

In an embodiment, at the cloud processing <NUM> of the server <NUM>, historical data <NUM> may be determined <NUM> to associate with multiple parameters based on the scan data <NUM> for a plurality of scanned components over time. Even before that, scan data <NUM> may be determined from raw data <NUM>. The testing model <NUM> may be generated <NUM> using the historical data <NUM>.

When having the testing model <NUM> in place, component data <NUM> may be received <NUM> for a scanned component <NUM>. The component <NUM> may be scanned using at least one ultrasonic probe at plant <NUM> to provide the component data <NUM>. The component data <NUM> may be combined <NUM> with the model <NUM>. Quality information <NUM> of the component <NUM> is then determined <NUM> using the testing model <NUM> and the component data <NUM>.

In an embodiment, the quality information <NUM> may be determined <NUM> in corresponding way using the local data processing <NUM> if available model <NUM> and data are arranged there, instead of, or in addition to the cloud processing <NUM>.

In an embodiment, the testing model <NUM> may be generated at the cloud server <NUM> using cloud processing <NUM> and updated <NUM> to local processing <NUM> periodically.

In an embodiment, the plant <NUM> collects, analyses and stores data <NUM> for each inspected component <NUM>. This data is available on a dedicated server <NUM> and can be further processed. Generated Test Model <NUM> is configured to classify the material quality and gives the possibility to: <NUM>) Define in objective and transparent way the quality of the component (e.g. forged Connecting rod) forgings (grade of the forging) through a newly defined synthetic indicator; <NUM>) Monitor and compare forgings quality trends within suppliers (statistical quality control); <NUM>) Implement simple and direct visual tools to manage the inspection results; <NUM>) Provide a new powerful tool to support Supply Chain development and achieve cost optimization and scrap rates reduction for conrod forgings; and <NUM>) Provide a new tool for Risk Management and in developing new design by systematically identifying critical areas.

In an embodiment, component suppliers <NUM> (a plurality of plant systems similar to <NUM>) may provide PAUT inspection data <NUM> collected from manufactured component <NUM> and load the data <NUM> to customer data base <NUM> at server <NUM>. Such data <NUM> may be immediately automatically processed, analysed and evaluated. This would mean that for each component <NUM> manufactured at supplier plant <NUM>, the overall quality could be defined before accepting its delivery to the customer plant <NUM> for installing to the apparatus <NUM>, such as an engine, for example. Such approach reduces internal inspection costs and time at plant <NUM> utilising the component <NUM> for the product <NUM> and assures that all parts <NUM> are in accordance with specifications before leaving supplier premises towards customer plant <NUM>.

In an embodiment, the test model <NUM> at server <NUM> may receive reference data from a remote apparatus (similar to system <NUM>-<NUM>, such as installed engine <NUM>, for example) comprising a plurality of reference probes or sensors operationally arranged to a reference system to provide reference data, and furthermore determining reference historical data based on the reference data; and providing the reference historical data as input to a neural network (see e.g. <FIG>).

The reference data may be received from a plurality of remote apparatuses each comprising a plurality of reference probes or sensors to provide reference data.

The reference data may relate to different operational conditions of a reference engine.

The reference data may relate to operational and environmental measurement data of the reference engine.

The reference data may be maintained at the server apparatus <NUM>, and further configured to dynamically updating the reference historical data based on the reference sensor data; and providing the reference historical data as input to the neural network (<FIG>).

In an embodiment, the method may comprise receiving environment data relating to a marine vessel; maintaining the environment data within the data storage system; determining historical environment data based on the environment data of the data storage system; and providing the historical environment data as input to the neural network.

In an embodiment, the reference sensor data relate to operational or environmental measurement data of a marine vessel. In case the reference sensor data comprises engine sensor data, the engine sensor data may comprise information on at least one of the following: number of engine starts; operating hours since last service; load cycles of the engine; miles travelled with the engine; amount of fuel used by the engine; and temperature data of the engine.

<FIG> shows a flow diagram showing operations in accordance with an example embodiment of the invention. In step <NUM>, a computer implemented method for determining material quality of a component, is started. In step <NUM>, ultrasonic scan data for a plurality of scanned components is received. In step <NUM>, the scan data is maintained within a data storage system. In step <NUM>, historical data associated with multiple parameters based on the scan data of the data storage system, is determined. In step <NUM>, a testing model is generated using the historical data, wherein the testing model is configured to define multiple quality ranges for each parameter. In step <NUM>, a component is scanned using at least one ultrasonic probe to provide component data. In step <NUM>, quality information of the component is determined using the testing model and the component data. The method is ended instep <NUM>.

<FIG> shows an example view on a component quality data item <NUM> according to an example embodiment of the invention.

In an embodiment, each scanned component <NUM> may be identified using unique identifier (ID) <NUM> and the determined quality information (QUAL) <NUM>, as disclosed in different embodiments, for example in <FIG>.

The quality data items <NUM> of all scanned components <NUM> may be maintained at cloud server <NUM>, <NUM> (see e.g. <FIG>) and whenever end-customer need any specific component <NUM>, the quality data item <NUM> and related information may be accessed and thus an appropriate component <NUM> with desired quality information may be selected.

In an embodiment, a reference piece may be used for US (ultrasonic) scanning. The reference piece may have different surfaces, shapes, holes, and a projections but the reference piece has no faults or the faults of the reference piece are only minor faults, which can be accepted. In other words the reference piece is the similar piece than the component to be checked without faults or with only minor faults. The reference piece is also scanned like the piece to be checked. So the scanning device for the reference piece is the same or similar with the ultrasonic device used for the piece to be checked.

In an embodiment, the Phased Array Ultrasonic Test (PAUT) according to embodiments enables that the collected data may used for statistic process control implementation to grade each single piece for quality monitoring. This opens the possibility to take preventive actions with component suppliers instead of scrapping defective pieces. Data Mining of collected data gives information about: Effects of heat treatment, Effects of forging mould on surface quality, Insights over raw bar indications, and Effects of equipment calibration for possible automatic compensations and better inspection reliability.

In an embodiment, acceptability of a component or piece can be/is based on the amount of indications, indication dimensions (size), amount of indications as combined area size of indications, to detections of indications close to each other, to material transparency, material cleanliness or to surface quality with acceptable combination rule using the testing model <NUM>. Indications may have passed through at least one criteria.

Without in any way limiting the scope, interpretation, or application of the claims appearing below, a technical effect of one or more of the example embodiments disclosed herein is improved method and apparatus for determining material quality of a component. Another technical effect of one or more of the example embodiments disclosed herein is that more convenient user experience from the point of operating user is enabled.

Another technical effect of one or more of the example embodiments disclosed herein is that a faster and more flexible solution is provided for performing ultrasonic scan data for a plurality of scanned components.

Another technical effect of one or more of the example embodiments disclosed herein is that system and service is cost efficient and easy-to-use.

Another technical effect of one or more of the example embodiments disclosed herein is that selection of components for different installations is optimised.

Another technical effect of one or more of the example embodiments disclosed herein is that quality inspection of forging component is improved.

Claim 1:
A computer implemented method for determining material quality of a forged component (<NUM>, <NUM>), the method comprising:
receiving (<NUM>) ultrasonic scan data (<NUM>) for a plurality of scanned forged components (<NUM>, <NUM>);
maintaining (<NUM>) the scan data (<NUM>) within a data storage system (<NUM>);
determining (<NUM>) historical data (<NUM>, <NUM>) associated with multiple parameters based on the scan data (<NUM>) of the data storage system (<NUM>);
generating (<NUM>) a testing model (<NUM>, <NUM>) using the historical data (<NUM>), wherein the testing model (<NUM>) is configured to define multiple quality ranges for each parameter;
scanning (<NUM>) a forged component (<NUM>, <NUM>) using at least one ultrasonic probe (<NUM>) to provide component data (<NUM>, <NUM>); and
determining (<NUM>) quality information (<NUM>) of the forged component (<NUM>, <NUM>) using the testing model (<NUM>) and the component data (<NUM>, <NUM>);
wherein said multiple parameters comprise at least two of following: ultrasonic wave attenuation and transparency, material surface quality and material cleanliness.