Patent Publication Number: US-2022221848-A1

Title: A method to restore the functional state of an automatic machine for the production or the packaging of consumer products

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This patent application claims priority from Italian patent application no. 102019000007581 filed on 30 May 2019, the entire disclosure of which is incorporated herein by reference. 
     TECHNICAL FIELD 
     This invention relates to a method to restore the functional state of an automatic machine for the production or the packaging of consumer products. 
     This invention finds advantageous application in restoring the functional state of an automatic packaging machine for packets, to which the following description specifically refers but without any loss of generality thereby. 
     PRIOR ART 
     An automatic packaging machine usually comprises a plurality of actuators that act on consumer products (e.g. cigarette packets, food, or sanitary pads, etc.) to change their shape, structure, or position. Generally, actuators are electric motors or pneumatic cylinders and are firmly connected to mechanical parts with different shapes and sizes designed to process the consumer products. 
     Due to the continuous increase in production volumes, these automatic machines are subject to increasing complexity, both with regard to mechanical parts, which have to occupy less and less space and perform increasingly complex operations, and with regard to the increase in the automatic machine&#39;s active components, such as motors and/or sensors. This increasing complexity makes it increasingly difficult to identify and solve the various faults that may occur due to dirt, wear, mechanical parts breakage, incorrect calibration, or inaccurate assembly of a component, etc. 
     This problem is aggravated in the case of several machines connected in a row. 
     The presence of manuals and catalogues relating to the resolution of faults, as well as increasingly sensorized and precise diagnostic systems, are not able, however, to suggest, with precision, a decisive solution for all possible faults, which allows the functional state to be restored in the shortest possible time, to an operator. Therefore, the identification and resolution of a fault leads to considerable losses in production time. 
     Moreover, in most cases, once a fault has been resolved, the solution is not recorded, shared, or associated with the operating condition of the automatic machine and, therefore, if a machine has a similar fault to one already resolved on another machine in the same or another plant, the solution must be sought independently in both cases, without being able to make use of the work already done for the other. 
     DESCRIPTION OF THE INVENTION 
     The purpose of this invention is to provide a method to restore the functional state of an automatic machine for the production or packaging of consumer products that is free of the drawbacks described above and, at the same time, is simple and economical to implement. 
     In accordance with this invention, a method is provided to restore the functional state of an automatic machine for the production or the packaging of consumer products, as claimed in the attached claims. An automatic machine for the production or the packaging of consumer products, designed to implement the above-mentioned method, is also provided. 
     The claims describe preferred embodiments of this invention, forming an integral part of this description. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       This invention will now be described with reference to the appended drawings, illustrating some non-limiting embodiments thereof, wherein: 
         FIG. 1  is a perspective and schematic view of an automatic packaging machine for the production of packets, 
         FIG. 2  schematically illustrates the structure and connection between a first and a second knowledge base, which in turn are connected to a data processing system and an interface; 
         FIG. 3  illustrates a possible flow chart of the general steps of the method and how they can be connected; and 
         FIGS. 4, 5, and 6  illustrate possible screens of the interface in  FIG. 2  through which an operator interacts with the data processing system. 
     
    
    
     PREFERRED EMBODIMENTS OF THE INVENTION 
       FIG. 1  illustrates an automatic machine  1  for the production of consumer products, in particular an automatic packaging machine  1  for the application of a transparent overwrap to cigarette packets. According to a first aspect of this invention, a method to restore the functional state of at least one part of the automatic machine  1  is provided. The term “functional state” refers to an operating condition of the automatic machine  1 , i.e. a condition wherein the automatic machine  1  is switched on and free of errors (faults, failures) that hinder its production. 
     The automatic machine  1  comprises a plurality of movable operating members designed to process consumer products (cigarette packets  2  in the embodiment illustrated in  FIG. 1 ). In particular, the automatic machine  1  comprises a part  3  (illustrated in more detail in  FIG. 4 ) equipped with a set of actuators. 
     According to some preferred but non-limiting embodiments, such as those illustrated in the attached figures, the actuators comprise electric motors (in particular brushless motors). According to other non-limiting embodiments, the actuators also comprise types of drives other than electric motors (for example, pneumatic or hydraulic cylinders, electric activation cylinders, etc.). 
     The automatic machine  1  also comprises a control unit  4  designed to control the state, position, and dynamics of the movable operating members (and, thus, the actuators as well) of the automatic machine  1 , and a data processing system  5 , designed to carry out complex computing and/or comparison operations and with a large number of variables. For example, the data processing system  5  can be a computer, a processor, or a circuit board, etc. 
     Advantageously, and as illustrated in the non-limiting embodiment in  FIG. 2 , the automatic machine  1  comprises a writable memory  6  (especially a non-volatile one), which is connected to the data processing system  5 , and a user interface device  7 , which is also connected to the data processing system  5 . 
     According to some non-limiting embodiments, which are not illustrated, the control unit comprises the processing system  5  and the writable memory  6 . 
     The automatic machine  1  (especially the part  3 ) has different components that may cause a plurality of faults. For example: a motor may cause a fault due to excessive current demand due to dirt or interference; a component may break or dismantle (e.g. due to vibrations) and cause a sudden lowering of the current demanded by a motor; a photocell may become dirty or generate errors due to incorrect calibration; a mechanical component may be mounted incorrectly or inaccurately and, therefore, have different inertial values than expected, or cause a certain number of consecutive rejects, etc. 
     The method in accordance with this invention comprises a one-time step for identifying a list of all the operating variables  8  that characterise the operation of the automatic machine  1  (as illustrated in  FIG. 2 ). 
     The term “operating variables” refers to all those values that indicate a condition of one part or of one component of the automatic machine  1 . 
     By way of non-limiting example, the following are to be considered operating variables  8 : the feedback of any sensor; the humidity of the environment wherein the automatic machine  1  is located; the value of an encoder; the status, position, speed, or acceleration of a motor; a machine status (alarm, warning, production, emptying, change of format, end of shift, etc.); a calibration parameter; an inertial parameter; an electrical current; the values of a relay; the values of a pneumatic system (vacuum suction); production counters (number of packets  2  produced, number of packets  2  discarded, material consumption, etc.); analogical control variables (e.g. glue level or temperature of a gumming machine, fill level or temperature of a lung, lubricating oil level and temperature, temperature of a heater or dryer or welder); digital control variables (e.g. presence of a new reel in an unwinding spindle); or the image from a quality inspection camera, etc. 
     In addition, the method comprises the step of storing, once and inside the writable memory  6 , a first knowledge base  9  containing a plurality of problematic operating conditions  10 , each consisting of a set of values associated with the operating variables  8  and of at least one corresponding known fault  11 . In other words, as illustrated in  FIG. 2 , a plurality of conditions  10  are stored inside the knowledge base  9  that correspond to various states of the automatic machine  1  in the presence of known faults  11 . The method comprises the additional step of training, once, the data processing system  5 , associating at least one solution  12  to each problematic operating condition  10  and storing, inside the writable memory  6 , a knowledge base  13  containing the solution. 
     The term “once” means “one time”. In particular, it means “each and every time the set of operating variables  8  is changed” (for example, due to the addition or removal of an actuator, the replacement of a sensor). In other words, each and every time an operating variable  8  is added or removed. In these cases, the addition or removal of problematic operating conditions  10  from the knowledge base  9  takes place (the same takes place with the solutions  12  stored in the knowledge base  13 ). For example, if a photocell is added for controlling the presence of a film, the knowledge bases  9  and  13  will be updated, including the case in which the film is present and in which the film is not present (thus, broken and needing to be reinserted in the machine). 
     In the non-limiting embodiment in  FIG. 2 , the first problematic operating condition  10  is associated with four solutions  12 , the second problematic operating condition  10  is associated with a single solution  12 , the third problematic operating condition  10  is associated with two solutions  12 , etc.; in general, each problematic operating condition  10  is associated with at least one solution  12 . Problematic operating conditions  10  are associated with several solutions  12  (two, three, or four, etc.) if the same problematic operating condition  10  represents the status of the automatic machine  1 , the cause of various known faults  11 . 
     Advantageously, but not necessarily, the steps illustrated up to this point are implemented during the development of the automatic machine  1 , in particular before the automatic machine  1  is sent to a customer or, in any case, before the automatic machine  1  enters (or re-enters after mechanical, electric, or software changes) into production. 
     Advantageously, the method comprises the additional step of identifying, through the control unit  4 , a current operating condition  14  consisting of a set of current operating variable  8  values. In other words, during this step, the control unit  4  registers the current values of the operating variables  8  and saves them. In some non-limiting cases, these current values are saved inside a volatile memory. In other, non-limiting cases, these values are saved inside a permanent memory, in particular in the writable memory  6 . 
     In some non-limiting embodiments, the current operating condition  14  is identified following an unknown fault  15 , or an alarm and/or stopping, of the automatic machine  1 . In particular, the unknown fault  15  of the automatic machine is shown to the operator through the interface device  7 . 
     In other non-limiting embodiments, the current operating condition  14  is identified during the production of the automatic machine  1 , to prevent any unknown fault  15 . For example, in the part  3  of the automatic machine  1  (illustrated in  FIG. 4 ), a wheel  21  is present, which can be monitored during production. In particular, it is possible to control the torque (or electric current) required by an electric motor that moves the wheel  21 , or the inertia detected by a drive that controls the motor. In this way, it is possible to check that the wheel is not subject to any slowing down due to dirt (for example, tobacco that escapes the packets  2  or glue that is spread during the movement of the packets  2  themselves) and that it is correctly mounted (that it has, in other words, a known inertia, in particular a constant one being a basically cylindrical body). 
     Advantageously, but not necessarily, the current operating condition  14  is identified at regular intervals in order to check that the current values of the operating variables  8  are correct, including during production, and that there aren&#39;t any imminent, unknown faults  15 . 
     According to a non-limiting embodiment, the values of the operating variables  8  are compared to the reference values in light of a tolerance range, with thresholds placed at distances equal or different to the reference value. 
     In the flow chart in  FIG. 3 , the convention is used according to which the oval blocks indicate the beginning or end of the chart, the rectangular ones indicate a generic instruction, and the rhomboid ones, placed at a branching off, are optional blocks, containing a logic condition that determines the direction the flow will take. In particular, at the optional blocks, the flow of the chart branches off in the direction marked by the check mark “✓” if the logic condition is satisfied; otherwise, if this condition is not satisfied, the flow branches off in the direction marked by the symbol “X”. 
     In use, as illustrated in the flow chart in  FIG. 3 , if the control unit  4  verifies the presence (or imminent presence) of an unknown fault  15  (initial block  22  in the chart), the method involves interrogating the operating variables  8  so as to determine the current operating condition  14  (block  23 ) of the automatic machine  1 . 
     Following this step, the method comprises the additional step (block  24 ) of searching, among all the problematic operating conditions  10  stored in the first knowledge base  9 , for the problematic operating condition  10  that is the most similar to the current operating condition  14 . 
     Advantageously, but not necessarily, in order to speed up the search for a solution  12 , the control unit  4  initially inspects the knowledge base  9  in the search for a (substantial) match (i.e. a match between operating variables  8  greater than at least, for example, 90%) between the current operating condition  14  and (at least) one problematic operating condition  10  (block  25 ). 
     In the non-limiting embodiment in  FIG. 3 , if there is at least one problematic operating condition  10  inside the knowledge base  9  that matches the current operating condition  14 , the method comprises the step of communicating (block  26 ) the solution  12  (or the solutions  12 ) associated with the matching problematic operating condition  10 , and stored inside the knowledge base  13 , to the operator O. 
     In the non-limiting embodiment of the attached figures, this communication takes place through the user interface device  7  (human-machine interface—HMI), which is of the video type, in particular with a touchscreen  19 , as illustrated in  FIGS. 1, 2, and 4-6 . 
     In other non-limiting embodiments that are not illustrated, the interface device  7  is of another type (for example, one capable of interpreting audio or movements). 
     According to some non-limiting embodiments that are not illustrated, the solutions  12  are communicated to the operator O (block  26 ) through a single interface device  7  screen and are (in particular) ordered in descending order starting with the solution  12  to the problematic operating condition  10  with the closest match to the current operating condition  14  and finishing with the solution  12  to the condition  10  most unlike (within a pre-established limit, for example, three or five) the condition  14 . In other words, the solutions  12  are provided all together to the operator O and ordered from the most probable to the least probable. 
     According to other non-limiting embodiments that are not illustrated, the solutions  12  are communicated to the operator O one at a time, through different screens, from the most probable to the least probable (block  26 ). 
     Advantageously but not necessarily, if there is an acceptable match (above a predefined threshold value) between the operating variables  8  of the current operating condition  14  and the operating variables  8  of at least one of the problematic operating conditions  10 , then if a corresponding solution  12  is communicated to the operator O, the data processing system  5  will ask the operator O to confirm the effectiveness of the communicated solution  12  (block  27 ). 
     In the non-limiting embodiment in  FIG. 3 , if the solution  12  communicated to the operator O in block  26  is not decisive, i.e. if the operator O replies negatively to the confirmatory request (block  27 ) regarding the effectiveness of the solution  12  proposed by the data processing system  5 , then the control unit  4  (or data processing system  5 ) checks for other solutions  12  (block  28 ) and, if this solution is present inside the knowledge base  13 , the data processing system  5  communicates an alternative solution  12  to the unknown fault  15  to the operator (the flow of the chart in  FIG. 3  returns to block  26 ). 
     In other cases, if the solution  12  communicated to the operator O is not correct and there are no alternative solutions  12  to the unknown fault  15  (or there are too many) inside the knowledge base  13 , then the data processing system  5  actively interacts with the operator O (block  29 ), in particular through the user interface device  7 , (asking for and) obtaining additional information and processing one or more additional solutions. 
     In the non-limiting embodiments in the attached figures, this interaction takes place through the video interface device  7 . In these cases, advantageously but not necessarily, the data processing system  5  asks the operator O to confirm the efficacy of one or more additional alternative solutions proposed as a result of the interaction. 
     According to the non-limiting embodiment illustrated in  FIG. 3 , as a result of the interaction between the data processing system  5  and the operator O (block  29 ), the data processing system  5  processes the new information obtained by the operator O to understand whether problematic operating conditions  10  similar to the current ones  14  exist in the knowledge base  9  (block  24 ). 
     According to other non-limiting embodiments that are not illustrated, as a result of the interaction between the data processing system  5  and the operator O (block  29 ), the data processing system  5  processes the new information obtained by the operator O in order to provide the operator with other solutions  12  (exiting block  29  you enter block  26  again). 
     In particular, if there are too many possible solutions  12 , the data processing system selects the most likely solutions  12 , using the information obtained from the interaction (block  29 ) between the operator O and the data processing system  5 . For example, if a pusher piston of the part  3  does not finish its stroke due to interference or dirt obstructing its passage, the data processing system  5  asks the operator O whether or not he sees dirt in the part  3  of the automatic machine  1 . Based on the operator&#39;s response, the data processing system discards one of the possible alternatives and proposes the correct one and how to resolve it (for example, replacing a piece or stopping the machine and cleaning the dirt). 
     Advantageously, but not necessarily, and as illustrated in the non-limiting embodiment in  FIGS. 2, 4, and 5  (screens), during the interaction (block  29 ,  FIG. 3 ), the data processing system  5  poses structured questions  17  to the operator O. 
     In the non-limiting embodiment in  FIGS. 2, 5, and 6 , such structured questions  17  are questions with closed answers. In particular, the operator O can only respond to the structured questions  17  by selecting one of the responses  18 . In this way, the data processing system  5  does not allow the operator O to respond by using a keyboard and prevents the operators&#39; differences in language or ways of expressing themselves from influencing the understanding of the unknown faults  15 . 
     Preferably, but not necessarily, the operator O selects one of the responses  18  through the interface device  7 . 
     Advantageously, but not necessarily, as illustrated in  FIGS. 4 and 6 , in order to speed up the method to restore the functional state of the machine  1 , during the interaction (block  29 ), the operator O indicates the part  3  of the automatic machine  1  subject to the unknown fault  15  to the data processing system  5 , especially through the user interface device  7 . 
     Advantageously, but not necessarily, the operator O indicates a sub-part of the part  3  of the automatic machine subject to the fault to the data processing system  5 . 
     In the non-limiting embodiment in  FIGS. 4 and 6 , the operator O indicates the part  3  based on a three-dimensional model  16  of the automatic machine  1 . In particular, the operator selects the part  3  through the interface device  7  (touching the touchscreen  19 ). In this way, the data processing system is able to exclude all the problematic operating conditions relating to other parts of the automatic machine  1  (and thus avoids proposing useless solutions  12  to the operator). 
     The operator can also select several parts  3  of the automatic machine  1  through the interface device  7 . This function is particularly useful since, given the complexity of the automatic machine  1 , the fact that a fault  15  occurs in one part  3  of the machine  1  does not mean that the cause of the fault  15  can be identified in the same part  3 . 
     Advantageously, but not necessarily, in the absence of solutions  12  inside the knowledge base  13  (thus, in the absence of problematic operating conditions  10  that match the current operating condition  14  determined by the unknown fault  15 ), the data processing system  5  processes the additional alternative solutions based on artificial intelligence algorithms (block  30  in  FIG. 3 ). 
     In other words, the data processing system  5  uses algorithms that are known in information science or artificial intelligence systems, such as, for example, decision trees, Bayes classifiers, support vector machines (SVM), set methods, statistical analyses (of the main components), or neural networks, to analyse the various operating conditions (problematic and current) to find a solution  12  associated with a problematic operating condition  10  similar to the current operating condition  14 . The term “similar” refers to those problematic operating conditions  10  that have at least 70% (in particular, 80%, more precisely, 90%) of the operating variable  8  values basically matching the operating variable  8  values of the current operating condition  14 . In any case, the remaining match percentage identifies missing information, which is, preferably, requested from the operator O, as described below. 
     Advantageously, but not necessarily, if the data processing system  5  finds at least one solution  12  that resolves a known fault  15  associated with a different problematic operating condition  10  that is different but similar to the current operating condition  14  (block  31 ), it is communicated to the operator O (the flow of the chart in  FIG. 3  returns to block  26  from bock  31 ). 
     As an alternative, or in addition, in the absence of solutions  12  inside the knowledge base  13  (thus, in the absence of problematic operating conditions  10  that match the current operating condition  14  determined by the unknown fault  15 ), the processing system  5  interacts with the operator O through the interface device  7  to acquire additional information on the status of the automatic machine  1  (block  32 ). 
     Advantageously, but not necessarily, the method comprises an additional step in which the data processing system  5  updates the knowledge base  9  and/or  13  and/or establishes connections between the elements inside the knowledge base  9  and/or  13  based on new alternative solutions  12  processed as a result of the interaction with the operator O or as a result of the processing through artificial intelligence algorithms. 
     In the non-limiting embodiment illustrated in the chart in  FIG. 3 , if none of the additional alternative solutions  12  processed by the data processing system  5  solve the unknown fault  15 , the data processing system  5  learns, from the responses  18  of the operator O to the structured questions  17  (in particular posed through decision tree systems), a new solution  20  for the unknown fault  15  and stores this new solution  20  inside the second knowledge base  13 , associating it with the current operating condition  14 , which is, in turn, stored inside the knowledge base  9  as a new known problematic operating condition  10 . 
     Advantageously, but not necessarily, the method comprises a machine learning step (block  33 ) performed by the data processing system  5  based on the information stored in the writable memory  6  and/or based on the information obtained from the interaction (block  32 ) between the data processing system  5  and the operator O, which is performed through the user interface device  7 , in particular based on the responses  18  of the operator O to the structured questions  17 . 
     In the non-limiting embodiment in  FIG. 6 , the data processing system  5  interrogates the operator O, asking which actions are (or were) necessary to resolve the unknown fault  15 . The actions requested by the data processing system  5  belong to a predefined set of known actions (adjustments, calibrations, manoeuvres, component or wrapping material replacement, removal of dirt or cause of flooding, lubricant or glue addition, etc.). However, if the predefined set of known actions does not contain the one necessary to resolve the unknown fault  15 , the data processing system  5  is able to learn new actions from the operator O and to propose them for the resolution of a successive unknown fault  15  through the user interface device  7 . 
     In this way, the data processing system  5  obtains information to make analyses based on learning algorithms known from computer technology, such as computational statistics, pattern recognition, artificial neural networks, adaptive filtering, dynamic systems theory, image processing, data mining, adaptive algorithms, etc. In other words, during the above-mentioned method, the data processing system  5  assesses the operating variables  8  that characterise the current operating condition  14  (constituted, precisely, of a set of current operating variable  8  values). The current operating condition  14  then determines the “problem to be solved”. In the event that there is no problematic operating condition  10  and no matching known solution inside, respectively, the first knowledge base  9  and the second knowledge base  13 , the processing system  5  recovers the missing information (not traced by the specific type of machine) from a similar problematic operating condition (as previously described) asking, for example, the operator O to choose among pre-classified alternatives in order to bridge the missing percentage and identify the most probable one of similar conditions. 
     In particular, this is the identification and insertion step for a new problematic condition  10 , until now unknown, in the knowledge base  9  of the problematic operating conditions  10 . 
     The method involves, thus, processing the above-mentioned missing information through one of the artificial intelligence techniques mentioned above. Specifically, when a new problem occurs, the processing system  5  must identify “similar problems”, in particular those already resolved beforehand and propose the matching solution to the operator O. More specifically, if the solution  12  were effective, a new problem (i.e. problematic operating condition)-solution pair would be inserted in the processing system  5 , or in the knowledge bases  9  and  13 , respectively. For example, the new problem-solution pair could be a new node of an artificial neural network or a new branch of a decision tree (or of a deep forest system). 
     Specifically, if the solution  12  highlighted were not present in the knowledge base  13  of solutions, the data processing system  5  ensures it is automatically inserted in the knowledge base  13  and for pairing the new solution  12  with the new problematic operating condition  10  identified. 
     In this way, the method self-supplies and improves its own efficacy with use. In detail, the presence of a first knowledge base  9  and of a second knowledge base  13  composed, respectively, of a certain number of problems (problematic operating conditions  10 ) and corresponding solutions  12 , is necessary. To construct and widen these knowledge bases  9  and  13 , part of the information is recuperated from the machine  1  during its operating cycle, others are requested from the operator O. 
     During the learning step, the method enables the information not automatically provided by the machine  1  to be integrated with human experience (by the operator O, based on the interventions and changes he/she makes). If there is a problem, the user interface (graphic) device  7  of the machine  1  proposes a choice among possible options to the operator O in order to thoroughly characterise both the current operating condition  14  and the solution  12 . 
     In some non-limiting embodiments, the transmission of information for the machine learning step, or for manual learning, following an unknown fault  15  for which the data processing system  5  has found no solution  12 , takes place through a direct connection between the user interface device  7  and the data processing system  5 . 
     In some non-limiting cases, the operator O interacts directly with the data processing system  5  through the interface device  7  to provide the data necessary for the machine learning step (block  33 ). In other non-limiting cases, the operator O interacts directly with the data processing system  5  by connecting a computer via cable or a short-range network, through which he/she provides the processing system  5  with the data necessary to perform the machine learning. In other non-limiting cases, the operator O interacts directly with the data processing system  5  by connecting a portable storage device (e.g. USB stick or SD card) to it. 
     In other non-limiting embodiments, the transmission of information for the machine learning step, or for manual learning, following an unknown fault  15  for which the data processing system  5  has found no solution  12 , takes place through a remote connection, in particular through distributed architecture (e.g. an intranet network, intranet, or cloud), between the user interface device  7  and the data processing system  5 . In this way, the operator O can immediately restart production following the restoration of the functional state, without losing time to provide information relating to the solution  12  activated to the data processing system  5 . For example, the operator O can connect to the automatic machine remotely once production has restarted in order to provide the system  5  with additional information for the machine learning step. 
     In particular, in use, when the learning step is considered sufficient, the data processing system  5 , communicating to the user interface device  7 , when a problem occurs is able to propose the solution matching the most similar “problem” previously “learned” to the operator O. 
     Advantageously, but not necessarily, the knowledge bases  9  and  13  are common to several automatic machines  1  of the same type and/or designed to produce the same product. In this way, it is possible to use more complete knowledge bases (trained by several different operators on different machines) and, thus, capable of providing effective solutions  12  and for a greater quantity of faults  15 . 
     According to an additional aspect of this invention, an automatic machine  1  for the production or the packaging of consumer products is provided, inside of the writable memory  6  of which the knowledge bases  9  and  13  are stored containing, respectively, a plurality of possible problematic operating conditions  10  (each associated with a known fault  11 ) of the automatic machine  1  and at least one solution  12  for each known fault  11 . In particular, the automatic machine  1  is designed to perform the method described above. 
     Although the invention described above makes particular reference to a very precise embodiment, it is not to be considered limited to such an embodiment, since it covers all those variants, modifications, or simplifications that would be evident to the expert in the field, such as: the addition of additional steps, different types of interaction, the use of models or algorithms other than those mentioned above, etc. 
     This invention has several advantages. 
     First of all, it improves the efficiency of restoring the functional state of an automatic machine for the production of consumer products caused by dirt, wear, breakage of mechanical parts, incorrect calibration, or incorrect assembly of a component. 
     In addition, this efficiency is increased without the need for an experienced operator to intervene to understand what the fault was and what caused it. In fact, it is the automatic machine itself that suggests which fault occurred to the operator and how to solve it. 
     In addition, the invention described above allows the automatic machine to be trained (independently or by an operator) so that, once a fault has occurred due to (for example) dirt, wear, incorrect assembly, or calibration of components, the machine itself can suggest the right solution to an operator when this fault occurs again. In this way, even if there has been a change of operator between the previous fault and the current one, the solution that has solved this fault in the past is, nonetheless, suggested. 
     In addition, this invention makes it possible to create a shared knowledge (common to several machines of the same type), which makes it possible to speed up the process of restoring the functional state because, if a fault has already been solved on any of the machines, this solution will be known to all the other machines, which will be able to provide it promptly to an operator in the event that this fault occurs again.