Patent Publication Number: US-2022214680-A1

Title: Predicting end of life for industrial automation components

Description:
BACKGROUND INFORMATION 
     The subject matter disclosed herein relates to end-of-life predictions for components and more specifically relates to end-of-life predictions by using environmental and usage data to modify a baseline lifetime model of a component of a system with physical devices. 
     BRIEF DESCRIPTION 
     A method for predicting end-of-life for a component is disclosed. An apparatus and computer program product the functions of the method. The method includes determining a baseline lifetime model for a component connected to a machine functional safety system. The component is part of a system with physical devices. The method includes monitoring environmental conditions and usage conditions of the component and modifying the baseline lifetime model based on the monitored environmental and usage conditions to produce a modified lifetime model for the component. The method includes tracking a lifetime progress of the component with respect to the modified lifetime model and sending an alert in response to lifetime progress of the component reaching a lifetime threshold associated with the modified lifetime model. 
     An apparatus for predicting end-of-life for a component includes a processor and a memory that stores program code executable by the processor to determine a baseline lifetime model for a component connected to a machine functional safety system, where the component is part of a system with physical devices, and monitor environmental conditions and usage conditions of the component. The program code is executable by the processor to modify the baseline lifetime model based on the monitored environmental and usage conditions to produce a modified lifetime model for the component, to track a lifetime progress of the component with respect to the modified lifetime model, and to send an alert in response to lifetime progress of the component reaching a lifetime threshold associated with the modified lifetime model. 
     A computer program product for predicting end-of-life for a component includes a computer readable storage medium having program code embodied therein. The program code is executable by a processor to determine a baseline lifetime model for a component connected to a machine functional safety system, where the component is part of a system with physical devices, to monitor environmental conditions and usage conditions of the component, and to modify the baseline lifetime model based on the monitored environmental and usage conditions to produce a modified lifetime model for the component. The program code is executable by a processor to track a lifetime progress of the component with respect to the modified lifetime model and to send an alert in response to lifetime progress of the component reaching a lifetime threshold associated with the modified lifetime model. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       In order that the advantages of the embodiments of the invention will be readily understood, a more particular description of the embodiments briefly described above will be rendered by reference to specific embodiments that are illustrated in the appended drawings. Understanding that these drawings depict only some embodiments and are not therefore to be considered to be limiting of scope, the embodiments will be described and explained with additional specificity and detail through the use of the accompanying drawings, in which: 
         FIG. 1  is a schematic block diagram of a system with physical devices for predicting end-of-life for a component according to an embodiment; 
         FIG. 2  is a schematic block diagram of an apparatus for predicting end-of-life for a component according to an embodiment; 
         FIG. 3  is a schematic block diagram of another apparatus for predicting end-of-life for a component according to an embodiment; 
         FIG. 4  is a flowchart diagram of a method for predicting end-of-life for a component according to an embodiment; 
         FIG. 5  is a flowchart diagram of another method for predicting end-of-life for a component according to an embodiment; and 
         FIG. 6  is a diagram illustrating a baseline curve for baseline lifetime model and a modified curve for a modified lifetime model for a component. 
     
    
    
     DETAILED DESCRIPTION 
     Reference throughout this specification to “one embodiment,” “an embodiment,” or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, appearances of the phrases “in one embodiment,” “in an embodiment,” and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment, but mean “one or more but not all embodiments” unless expressly specified otherwise. The terms “including,” “comprising,” “having,” and variations thereof mean “including but not limited to” unless expressly specified otherwise. An enumerated listing of items does not imply that any or all of the items are mutually exclusive and/or mutually inclusive, unless expressly specified otherwise. The terms “a,” “an,” and “the” also refer to “one or more” unless expressly specified otherwise. The term “and/or” indicates embodiments of one or more of the listed elements, with “A and/or B” indicating embodiments of element A alone, element B alone, or elements A and B taken together. 
     Furthermore, the described features, advantages, and characteristics of the embodiments may be combined in any suitable manner. One skilled in the relevant art will recognize that the embodiments may be practiced without one or more of the specific features or advantages of a particular embodiment. In other instances, additional features and advantages may be recognized in certain embodiments that may not be present in all embodiments. 
     These features and advantages of the embodiments will become more fully apparent from the following description and appended claims or may be learned by the practice of embodiments as set forth hereinafter. As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method, and/or computer program product. Accordingly, aspects of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module,” “system” or the like. Furthermore, aspects of the present invention may take the form of a computer program product embodied in one or more computer readable medium(s) having program code embodied thereon. 
     Many of the functional units described in this specification have been labeled as modules, in order to more particularly emphasize their implementation independence. For example, a module may be implemented as a hardware circuit comprising custom VLSI circuits or gate arrays, off-the-shelf semiconductors such as logic chips, transistors, or other discrete components. A module may also be implemented in programmable hardware devices such as field programmable gate arrays, programmable array logic, programmable logic devices or the like. 
     Modules may also be implemented in software for execution by various types of processors. An identified module of program code may, for instance, comprise one or more physical or logical blocks of computer instructions which may, for instance, be organized as an object, procedure, or function. Nevertheless, the executables of an identified module need not be physically located together but may comprise disparate instructions stored in different locations which, when joined logically together, comprise the module and achieve the stated purpose for the module. 
     Indeed, a module of program code may be a single instruction, or many instructions, and may even be distributed over several different code segments, among different programs, and across several memory devices. Similarly, operational data may be identified and illustrated herein within modules and may be embodied in any suitable form and organized within any suitable type of data structure. The operational data may be collected as a single data set or may be distributed over different locations including over different storage devices, and may exist, at least partially, merely as electronic signals on a system or network. Where a module or portions of a module are implemented in software, the program code may be stored and/or propagated on in one or more computer readable medium(s). 
     The computer readable medium may be a tangible computer readable storage medium storing the program code. The computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, holographic, micromechanical, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. 
     More specific examples of the computer readable storage medium may include but are not limited to a portable computer diskette, a hard disk, a random access memory (“RAM”), a read-only memory (“ROM”), an erasable programmable read-only memory (“EPROM” or Flash memory), a portable compact disc read-only memory (“CD-ROM”), a digital versatile disc (“DVD”), an optical storage device, a magnetic storage device, a holographic storage medium, a micromechanical storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, and/or store program code for use by and/or in connection with an instruction execution system, apparatus, or device. 
     The computer readable medium may also be a computer readable signal medium. A computer readable signal medium may include a propagated data signal with program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electrical, electro-magnetic, magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport program code for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable signal medium may be transmitted using any appropriate medium, including but not limited to wire-line, optical fiber, Radio Frequency (RF), or the like, or any suitable combination of the foregoing 
     In one embodiment, the computer readable medium may comprise a combination of one or more computer readable storage mediums and one or more computer readable signal mediums. As used herein, a computer readable storage medium is a non-transitory computer readable storage medium. For example, program code may be both propagated as an electro-magnetic signal through a fiber optic cable for execution by a processor and stored on RAM storage device for execution by the processor. 
     Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Python, Ruby, R, Java, Java Script, Smalltalk, C++, C sharp, Lisp, Clojure, PHP or the like and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The program code may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a local area network (“LAN”) or a wide area network (“WAN”), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). The computer program product may be shared, simultaneously serving multiple customers in a flexible, automated fashion. 
     The computer program product may be integrated into a client, server and network environment by providing for the computer program product to coexist with applications, operating systems, machine functional safety systems and network operating systems software and then installing the computer program product on the clients and servers in the environment where the computer program product will function. In one embodiment software is identified on the clients and servers including the network operating system where the computer program product will be deployed that are required by the computer program product or that work in conjunction with the computer program product. This includes the network operating system that is software that enhances a basic operating system by adding networking features. In another embodiment, the computer program product is at least partially deployed on a machine functional safety system. 
     Furthermore, the described features, structures, or characteristics of the embodiments may be combined in any suitable manner. In the following description, numerous specific details are provided, such as examples of programming, software modules, user selections, network transactions, database queries, database structures, hardware modules, hardware circuits, hardware chips, etc., to provide a thorough understanding of embodiments. One skilled in the relevant art will recognize, however, that embodiments may be practiced without one or more of the specific details, or with other methods, components, materials, and so forth. In other instances, well-known structures, materials, or operations are not shown or described in detail to avoid obscuring aspects of an embodiment. 
     The embodiments may transmit data between electronic devices. The embodiments may further convert the data from a first format to a second format, including converting the data from a non-standard format to a standard format and/or converting the data from the standard format to a non-standard format. The embodiments may modify, update, and/or process the data. The embodiments may store the received, converted, modified, updated, and/or processed data. The embodiments may provide remote access to the data including the updated data. The embodiments may make the data and/or updated data available in real time. The embodiments may generate and transmit a message based on the data and/or updated data in real time. The embodiments may securely communicate encrypted data. The embodiments may organize data for efficient validation. In addition, the embodiments may validate the data in response to an action and/or a lack of an action. 
     Aspects of the embodiments are described below with reference to schematic flowchart diagrams and/or schematic block diagrams of methods, apparatuses, systems, and computer program products according to embodiments of the invention. It will be understood that each block of the schematic flowchart diagrams and/or schematic block diagrams, and combinations of blocks in the schematic flowchart diagrams and/or schematic block diagrams, can be implemented by program code. The program code may be provided to a processor of a general purpose computer, special purpose computer, sequencer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the schematic flowchart diagrams and/or schematic block diagrams block or blocks. 
     The program code may also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function/act specified in the schematic flowchart diagrams and/or schematic block diagrams block or blocks. 
     The program code may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the program code which executed on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The schematic flowchart diagrams and/or schematic block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of apparatuses, systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the schematic flowchart diagrams and/or schematic block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions of the program code for implementing the specified logical function(s). 
     It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. Other steps and methods may be conceived that are equivalent in function, logic, or effect to one or more blocks, or portions thereof, of the illustrated Figures. 
     Although various arrow types and line types may be employed in the flowchart and/or block diagrams, they are understood not to limit the scope of the corresponding embodiments. Indeed, some arrows or other connectors may be used to indicate only the logical flow of the depicted embodiment. For instance, an arrow may indicate a waiting or monitoring period of unspecified duration between enumerated steps of the depicted embodiment. It will also be noted that each block of the block diagrams and/or flowchart diagrams, and combinations of blocks in the block diagrams and/or flowchart diagrams, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and program code. 
     As used herein, a list using the terminology “one or more of” includes any single item in the list or a combination of items in the list. For example, one or more of A, B and C includes only A, only B, only C, a combination of A and B, a combination of B and C, a combination of A and C or a combination of A, B and C. As used herein, a list using the terminology “one of” includes one and only one of any single item in the list. For example, “one of A, B and C” includes only A, only B or only C and excludes combinations of A, B and C. 
     The description of elements in each figure may refer to elements of proceeding figures. Like numbers refer to like elements in all figures, including alternate embodiments of like elements. 
     A method for predicting end-of-life for a component is disclosed. An apparatus and computer program product the functions of the method. The method includes determining a baseline lifetime model for a component connected to a machine functional safety system. The component is part of a system with physical devices. The method includes monitoring environmental conditions and usage conditions of the component and modifying the baseline lifetime model based on the monitored environmental and usage conditions to produce a modified lifetime model for the component. The method includes tracking a lifetime progress of the component with respect to the modified lifetime model and sending an alert in response to lifetime progress of the component reaching a lifetime threshold associated with the modified lifetime model. 
     In some embodiments, the method includes using machine learning to contribute to derivation of the baseline lifetime model that is modifiable and applicable to the component of the system with physical devices. In further embodiments, the machine learning includes tracking a lifetime progress of a plurality of components similar to the component of the system with physical devices and tracking usage conditions, environmental conditions, and failures of the plurality of components to derive modifications applicable to the baseline lifetime model based on the usage conditions and applicable environmental conditions of the component of the system with physical devices. 
     In some embodiments, the method includes contributing to derivation of the baseline lifetime model that is modifiable and applicable to the component of the system with physical devices by testing a plurality of components similar to the component of the system with physical devices under various usage and environmental conditions and tracking failures of the plurality of components. In other embodiments, the method includes contributing to derivation of the baseline lifetime model that is modifiable and applicable to the component of the system with physical devices by gathering information from customers reporting component failures of components similar to the component of the system with physical devices and gathering usage information and environmental information where the components were installed. 
     In some embodiments, the component of the system with physical devices is a safety device connected to the machine functional safety system. In other embodiments, the usage conditions include operating cycles, current, voltage and/or power usage of the component. In other embodiments, the environmental conditions include vibration, temperature, humidity and/or chemicals present where the component is installed. In other embodiments, the lifetime threshold includes a point in the modified lifetime model indicative of the component approaching a rapid increase in probability of failure of the component. In other embodiments, the component includes an interlock switch, a non-contact switch, a limit switch, a light curtain, a cable pull switch and/or a mechanical switch. 
     An apparatus for predicting end-of-life for a component includes a processor and a memory that stores program code executable by the processor to determine a baseline lifetime model for a component connected to a machine functional safety system, where the component is part of a system with physical devices, and monitor environmental conditions and usage conditions of the component. The program code is executable by the processor to modify the baseline lifetime model based on the monitored environmental and usage conditions to produce a modified lifetime model for the component, to track a lifetime progress of the component with respect to the modified lifetime model, and to send an alert in response to lifetime progress of the component reaching a lifetime threshold associated with the modified lifetime model. 
     In some embodiments, the apparatus includes program code executable by the processor to use machine learning to contribute to derivation of the baseline lifetime model that is modifiable and applicable to the component of the system with physical devices. In further embodiments, the machine learning includes tracking a lifetime progress of a plurality of components similar to the component of the system with physical devices and tracking usage conditions, environmental conditions, and failures of the plurality of components to derive modifications applicable to the baseline lifetime model based on the usage conditions and applicable environmental conditions of the component of the system with physical devices. 
     In some embodiments, the apparatus includes program code executable by the processor to contribute to derivation of the baseline lifetime model that is modifiable and applicable to the component of the system with physical devices by testing a plurality of components similar to the component of the system with physical devices under various usage and environmental conditions and tracking failures of the plurality of components. In other embodiments, the apparatus includes program code executable by the processor to contribute to derivation of the baseline lifetime model that is modifiable and applicable to the component of the system with physical devices by gathering information from customers reporting component failures of components similar to the component of the system with physical devices and gathering usage information and environmental information where the components were installed. 
     In some embodiments, the usage conditions include operating cycles, current, voltage and/or power usage of the component. In other embodiments, the environmental conditions include vibration, temperature, humidity and/or chemicals present where the component is installed. In other embodiments, the lifetime threshold includes a point in the modified lifetime model indicative of the component approaching a rapid increase in probability of failure of the component. In other embodiments, the component includes an interlock switch, a non-contact switch, a limit switch, a light curtain, a cable pull switch and/or a mechanical switch. 
     A computer program product for predicting end-of-life for a component includes a computer readable storage medium having program code embodied therein. The program code is executable by a processor to determine a baseline lifetime model for a component connected to a machine functional safety system, where the component is part of a system with physical devices, to monitor environmental conditions and usage conditions of the component, and to modify the baseline lifetime model based on the monitored environmental and usage conditions to produce a modified lifetime model for the component. The program code is executable by a processor to track a lifetime progress of the component with respect to the modified lifetime model and to send an alert in response to lifetime progress of the component reaching a lifetime threshold associated with the modified lifetime model. 
     In some embodiments, the program code is executable to use machine learning to contribute to derivation of the baseline lifetime model that is modifiable and applicable to the component of the system with physical devices. In further embodiments, the machine learning includes tracking a lifetime progress of a plurality of components similar to the component of the system with physical devices and tracking usage conditions, environmental conditions, and failures of the plurality of components to derive modifications applicable to the baseline lifetime model based on the usage conditions and applicable environmental conditions of the component of the system with physical devices. 
       FIG. 1  is a schematic block diagram of a system  100  for predicting end-of-life for a component according to an embodiment. The system  100  includes a lifetime apparatus  102  in a controller  104 , an input/output (“IO”)-link  105 , a human-machine interface  106 , a manufacturing line  108  with assembly/processing equipment  110 , a conveyor belt  112 , parts  114  being manufactured, a parts bin  116 , access doors  118 ,  120 , an opening  121 , a safety relay  122 , a network interface  124 , connection taps  126 , trunk line conductors  128 , tap conductors  130 , a non-contact switch  132 , a light curtain  133 , locking switch  134 , a vibration sensor  135 , an emergency stop  136 , a temperature sensor  137 , a humidity sensor  138 , a terminator  139 , a computer network  140 , a server  142  and a graphical user interface and input/output devices  144 , which are described below. 
     The lifetime apparatus  102  determines a baseline lifetime model for a component, such as a locking switch  134 , of a system with physical devices, like the manufacturing line  108 . The component includes one or more types of wear-out mechanisms so that predicting end-of-life for the component is useful to reduce safety issues and equipment down time. The baseline lifetime model may be modified based on environmental conditions around the component and for usage of the component. The lifetime apparatus  102  monitors conditions the environmental conditions around the component and usage of the component and modifies the baseline lifetime model to derive a modified lifetime model. For example, where elevated temperature negatively affects the component, the baseline lifetime model may be adjusted to have a reduced lifetime for the component. The lifetime apparatus  102  tracks progress of the component with respect to the modified lifetime model and sends one or more appropriate alerts in response to the component reaching one or more lifetime thresholds associated with the modified lifetime model. The lifetime apparatus  102  is described in more detail below with regard to the apparatuses  200 ,  300  of  FIGS. 2 and 3 . 
     The lifetime apparatus  102 , in some embodiments, is in a controller  104 . For example, the controller may a be Logix S000™ Controller by Rockwell Automation® or similar controller. In other embodiments, the controller  104  is a computing device capable of executing program code. The controller  104 , in some embodiments includes a processor and memory coupled to the processor. In the embodiment, the lifetime apparatus  102  may be implemented with program code stored on computer readable storage media, such as a hard disk drive (“HDD”), solid-state storage (“SSD”), or other non-volatile storage where the program code may be loaded into volatile memory, such as dynamic random access memory (“DRAM”) or other cache accessible to the processor for execution. In other embodiments, all or a portion of the lifetime apparatus  102  is in the safety relay  122 . In other embodiments, all or a portion of the lifetime apparatus  102  is in the IO-link  104 . In some embodiments, at least a portion of the lifetime apparatus  102  is in a safety device, such as a non-contact switch  132 , a light curtain  133 , a locking switch  134 , or other safety device. 
     In other embodiments, the controller  104  is implemented using a programmable hardware device, such as a field programmable gate array (“FPGA”), programmable logic array, etc. for execution of the lifetime apparatus  102  In other embodiments, the controller  104  includes hardware circuits, such as custom VLSI circuits, gate arrays, etc. for implementation of the lifetime apparatus  102 . The controller  104  may include a network interface card, memory, data buses, a peripheral component interconnect express (“PCIe”) bus, data storage, input/output connections, and other components known to those of skill in the art. In other embodiments, the controller  104  is implemented using a combination of hardware circuits, a programmable hardware device, and/or a processor with memory. One of skill in the art will recognize other ways to implement the lifetime apparatus  102  on a controller  104 . 
     The controller  104  and lifetime apparatus  102  are part of a machine functional safety system  101 , such as a GuardLink® system by Rockwell Automation® or other machine functional safety system or industrial control system. The machine functional safety system  101  includes safety devices and other devices that are installed based on a risk assessment of conditions of a system with physical devices, such as the manufacturing line  108 , to prevent injury, monitor conditions, and to minimize down time of the mechanical system. The machine functional safety system  101  may be used to prevent injury from various types of systems with physical devices, such as manufacturing equipment, electrical equipment, motors, gears, sprayers, chemical process equipment, and the like. In the embodiment of the system  100  of  FIG. 1 , the machine functional safety system  101  includes a safety relay  122 , a network interface  124 , connection taps  126 , trunk line conductors  128 , tap conductors  130 , a non-contact switch  132 , a light curtain  133 , a locking switch  134 , an emergency stop  136 , a terminator  139 , and other safety devices, sensors, actuators, switches, etc. that are part of a machine functional safety system  101 . 
     A condition monitoring system includes an IO-link block  105  connected to environmental sensors, such as a vibration sensor  135 , a temperature sensor  137 , a humidity sensor  138 , a pressure sensor (not shown), a chemical sensor (not shown), and/or the like, which is connected to the lifetime apparatus  102  and/or to the controller  104 . In other embodiments, the environmental sensors are monitored by a system external to the condition monitoring system which may provide data to the lifetime apparatus  102  and/or controller  104 . In some embodiments, one or more of the environmental sensors are part of the machine functional safety system  101 . For example, the vibration sensor  135  may be connected to a connection tap  126  or may provide data to the connection tap  126  on a same tap conductor  130  as a safety device. For example, the vibration sensor  135  may be built into the safety device. In other embodiments, some components, such as the vibration sensor  135  are external to the condition monitoring system and/or machine functional safety system  101  and are able to provide data to the lifetime apparatus  102 , controller  104  and/or to the condition monitoring system. 
     The system  100 , in some embodiments, includes a human-machine interface (“HMI”)  106 , such a control panel, at or near the manufacturing line  108  to allow a user to control and interact with the controller  104  to control the machine functional safety system  101 . The HMI  106  may include a display screen and a means to receive user input. In other embodiments, the condition monitoring system includes an HMI  106 . 
     The manufacturing line  108  is merely representative of a system with physical devices that may be monitored by a condition monitoring system and/or machine functional safety system  101  that includes the lifetime apparatus  102 . The manufacturing line  108  depicted in  FIG. 1  includes assembly/processing equipment  110  and a conveyor belt  112  that interact with parts  114  being manufactured. In other embodiments, the system with physical devices may include a boiler, a gas turbine, electrical equipment, chemical processing equipment or any other system that can benefit from a condition monitoring system and/or machine functional safety system  102  depicted in the system  100  of  FIG. 1 . 
     The manufacturing line  108 , as with most mechanical systems or other system with physical devices, has inherent dangers as well as equipment that may fail. The machine functional safety system  101  includes components that enable monitoring of hazardous conditions, equipment health, environmental conditions, etc. to increase safety for personnel, to predict and/or detect equipment failure and/or to predict end of life of components, such as the safety devices of the machine functional safety system  101 . In some embodiments, the components of the machine functional safety system  101  help to improve performance of the manufacturing line  108  or other system with physical devices. In some embodiments, the machine functional safety system  101  includes safety devices, sensors and other components that are external to equipment within the manufacturing line  108 . In other embodiments, the machine functional safety system  101  receives input from equipment within the manufacturing line  108 /system with physical devices. In some embodiments, the manufacturing line  108  includes a condition monitoring system with environmental sensors that are used to monitor the safety devices of the machine functional safety system  101 . 
     In some embodiments, the machine functional safety system  101  includes a network interface  124  connected to a safety relay  122 . The network interface  124  provides a network connection to the controller  104 . For example, the machine functional safety system  101  may include one internet protocol (“IP”) address and may be able to provide information from safety devices through the single IP address to the controller  104 . Such an arrangement beneficially reduces the number of IP addresses for a plant that includes the manufacturing line  108  or other system with physical devices. Other networking interfaces  124  may include more than one IP address, for example, for multiple safety relays  122  or multiple lines from a safety relay  122 . A safety device may include a non-contact switch  132 , a light curtain  133 , a locking switch  134 , an emergency stop  136 , an actuator, a cable pull switch, a key interlock switch, and the like. In other embodiments, one or more safety devices include an IP address. In other embodiments, the safety devices run on a proprietary network different than an IP network. In other embodiments, the 10-link  105  or other device of the condition monitoring system includes an IP address and is connected over a computer network to the controller  104 , lifetime apparatus  102  or other device. 
     In the embodiment depicted in  FIG. 1 , the machine functional safety system  101  includes trunk line conductors  128  running between connection taps  126 . At each connection tap  126 , a tap conductor  130  runs to a safety device, such as a non-contact switch  132 , a light curtain  133 , a locking switch  134 , an emergency stop  136 , a cable pull switch, etc. In other embodiments, a connection tap  126  is connected to other equipment that provides data, such as a vibration sensor  135 . In other embodiments, the condition monitoring system is connected to a vibration sensor  135 , an acoustic sensor (not shown), a temperature sensor  137 , a humidity sensor  138 , a pressure monitor (not shown), or the like. In one embodiment, the machine functional safety system  101  includes a GuardLink® system by Rockwell Automation® or similar machine functional safety system by another vendor. A safety relay  122  in a GuardLink system, in some embodiments, has capacity for two lines where each line can have up to 32 safety devices. Other machine functional safety systems  101  may include multiple safety relays  122 , which would increase a capacity of the machine functional safety system  101  to include more safety devices. A GuardLink system has an ability to daisy chain between connection taps  126  without having to loop the trunk line conductor  128  in a loop while meeting applicable safety standards, such being EN/ISO 13849-1 performance level “e” (“PLe”) certified by TÜVRheinland® or other applicable certification. Other machine functional safety systems  101  may include a lifetime apparatus  102  and include other features and benefits. 
     In the system  100  of  FIG. 1 , the non-contact switch  132  is on an access door  118  and may be used to monitor when the access door is open. In some embodiments, the machine functional safety system  101  may send an alert when the non-contact switch  132  senses that the access door  118  is open, which may trigger shutdown of the manufacturing line  108  or other action. In the system  100  of  FIG. 1 , a light curtain  133  protects an opening  121  so that if an object, such as a hand interrupts a beam of light from the light curtain  133 , the machine functional safety system  101  sends an alert. In the system  100  of  FIG. 1 , a locking switch  134  maintains an access door closed until a signal releases the locking switch  134  and an emergency stop  136  senses a button push that triggers the machine functional safety system  101  to send an alert to shut down the manufacturing line  108  or other alert. Other machine functional safety systems  101  include other safety devices. The terminator  139  is placed on a terminal of the last connection tap  126  to signal to the machine functional safety system  101  that there are no more safety devices beyond the terminator  139 . 
     The vibration sensor  135  is positioned to monitor vibrations on the locking switch  134  and transmits vibration data to the controller  104  through an IO-link  105 . In other embodiments, the vibration sensor  135  transmits data over a same tap conductor  130  and connection tap  126  as the locking switch  134  and may be built into the locking switch  134 . In other embodiments, the vibration sensor  135  transmits vibration data over a system different than the machine functional safety system  101  and the vibration data is accessible to the lifetime apparatus  102 . In other embodiments, one or more other sensors, such as an acoustic sensor, monitor conditions at the locking switch  134 . In other embodiments, other components or safety devices, such as the non-contact switch  134 , the light curtain  133 , etc. have a vibration sensor  135  or other sensor. 
     In some embodiments, a system different than the condition monitoring system monitors various conditions that would alter a lifetime of a component. For example, a building monitoring system may monitor temperature, humidity, pressure, vibrations, acoustic noise, etc. and may provide input to the lifetime apparatus  102 . In some embodiments, the temperature sensor  137 , humidity sensor  138 , chemical sensor, etc. are located in a space where the component being monitored is located. In other embodiments, the, the temperature sensor  137 , humidity sensor  138 , chemical sensor, etc. are located adjacent to the component being monitored. 
     In the system  100  of  FIG. 1 , the condition monitoring system includes an IO-link that is connected to environmental sensors and transmits data to the lifetime apparatus  102 . In some embodiments, each environmental sensor is connected to a different IO-link  105 . In other embodiments, the IO-link  105  is connected to the lifetime apparatus  102  over a network and in some embodiments the IO-link  105  includes an IP address. In some embodiments, the IO-link  105  includes some or all of the lifetime apparatus  102  and transmits data to the controller  104 , such as an alert or other lifetime data associated with a component. 
     In the system  100  of  FIG. 1 , the controller  104  is connected to a server  142  over a computer network  140 . The controller  104  may communicate with the server  142  for various purposes. For example, the server  142  may control at least some aspects of a system with physical devices, such as the manufacturing line  108 . For example, the server  142  may be in contact with one or more motor controllers of the manufacturing line  108  and may control starting and stopping of the manufacturing line  108 . In other embodiments, the controller  104  controls the manufacturing line  108  and the server  142  may allow remote access. One of skill in the art will recognize other purposes for the server  142  and configurations to communicate with and control the manufacturing line  108 . 
     In some embodiments, some or all of the lifetime apparatus  102  is located in a server  142 , a cloud computing system, or similar computing device removed from the controller  104 , machine functional safety system  101  and system with physical devices and communicates with the controller  104 , machine functional safety system  101  and/or system with physical devices to gather information useful in deriving a baseline lifetime model of a component or similar components, to monitor usage and environmental conditions, to modify the baseline lifetime model, to track progress of a component on a modified lifetime model and/or to send alerts. 
     In some embodiments, the controller  104  is connected to or includes a graphical user interface (“GUI”) and input/output devices  144  that allow a user to interact with the lifetime apparatus  102  of the controller  104  to enter and view information. For example, the GUI and input/output devices  144  may be an electronic display, keyboard, mouse, etc. In other embodiments, a user may interact with the lifetime apparatus  102  via the HMI  106  and/or the server  142 . In some examples, in conjunction with setup of a component, such as a safety device in the machine functional safety system  101 , the controller  104  displays a lifetime model user interface to provide information about the component, the location of the component, sensors associated with the component, and the like. The lifetime model user interface facilitates entry of the parameters relevant to building a baseline lifetime model for the component being monitored. For example, when the component is first added to a system, such as the machine functional safety system  101 , the user interface may prompt a user involved in installing the component to enter data regarding the component, environment of the component, identification of sensors, such as a vibration sensor  135 , monitoring conditions associated with the component being monitored, and the like. In other embodiments, the user interface allows updating or adding component information after setup of the component. 
     Typically, the machine functional safety system  101  is designed using a risk assessment, which may include a projected useful lifetime of various components. The risk assessment may include a risk assessment for various parts of a manufacturing line  108  or other mechanical system with physical devices. For example, a portion of the risk assessment may be directed to the opening  121  that allows access to processing equipment  110 . The risk assessment may take into account information such as distance from the opening  121  to the processing equipment  110 , a hazard level for the processing equipment  110  accessible via the opening  121 , an amount of time required to stop the processing equipment  110  or whole manufacturing line  108 , delay from the time that the light curtain  133  is triggered until an alert is sent to controls of the manufacturing line  108 , etc. Spacing of beams of light of the light curtain  133  may be categorized as finger penetration, hand penetration, body penetration, etc. For example, one light curtain may be triggered when a finger penetrates the light curtain while another light curtain may be triggered when a hand penetrates the light curtain. The risk assessment takes into account the type of light curtain  133  installed. The risk assessment may require beam spacing for hand penetration where there is sufficient time to stop the hazardous equipment accessible through the opening  121  when a hand reaches through the opening  121 . 
     An important part of risk, especially for critical equipment, is a prediction of how long a particular component may last before failing. A useful lifetime of a component may initially include particular operating conditions, a rate of use of the component, and the like. In some instances, a baseline lifetime model may be derived in ideal conditions or basic laboratory conditions and may not take into account extremes in temperature, humidity, voltage, current, vibration, etc. 
       FIG. 6  is a diagram illustrating a baseline curve  602  for baseline lifetime model and a modified curve  604  for a modified lifetime model for a component. The baseline curve  602  and modified curve  604  are typical “bathtub” curves where during a burn-in period where there is initially an elevated failure rate λ, a useful life period where the failure rate λ is the useful life failure rate λ useful life , which is the reciprocal of mean time before failure (“MTBF”), and a wear-out period where the failure rate λ increases typically exponentially. Where conditions are adverse for the component, the baseline curve  602  may change to have a shorter useful life period, as indicated by the modified curve  604 . For example, a lower voltage for a switch may cause increased debris build-up and quicker failure of the switch, which then results in a lower useful life period (e.g. useful life period 2). Other factors that may reduce the useful life period of a component may include temperature, humidity, pressure, voltage, current, higher than expected usage, certain chemicals in the air, etc. The modified curve  604  may be the result of one environmental or usage condition or may be a result of two or more environmental or usage conditions. 
       FIG. 2  is a schematic block diagram of an apparatus  200  for predicting end-of-life for a component according to an embodiment. The apparatus  200  includes one embodiment of the lifetime apparatus  102  with a baseline lifetime model module  202 , an environmental conditions module  204 , a lifetime model modification module  206 , a lifetime tracking module  208  and an alert module  210 , which are described in more detail below. The lifetime apparatus  102  may be implemented, in one embodiment, as program code stored on computer readable storage media executable by a processor. In other embodiments, the lifetime apparatus  102  may be implemented by a programmable hardware device. In other embodiments, the lifetime apparatus  102  is at least in part implemented using hardware circuits. In other embodiments, the lifetime apparatus  102  is implemented using a combination of program code, a programmable hardware device and/or hardware circuits. 
     The apparatus  200  includes a baseline lifetime model module  202  configured to determine a baseline lifetime model for a component connected to a machine functional safety system  101 . The component is part of a system with physical devices, such as the manufacturing line  108 . In some embodiments, the component is a safety device. The baseline lifetime model, in some embodiments, is a lifetime model provided by a manufacturer of the component or is derived from information from the manufacturer. In other embodiments, the baseline lifetime model is adjusted for expected operating conditions of the component. For example, the baseline lifetime model may be based on a particular expected temperature range. 
     In some embodiments, the baseline lifetime model includes equations that include input variables of one or more environmental conditions and usage conditions. For example, the baseline lifetime model may include one or more equations that produce the baseline curve  602  of  FIG. 6  and may include inputs of one or more of temperature, humidity, pressure, number of operational cycles, voltage, current, etc. that change the baseline curve  602  to a modified curve  604 . The baseline lifetime model may adjust slopes, failure probability during useful life, thresholds such as the wear-out time t w , and the like. 
     The apparatus  200  includes an environmental conditions module  204  configured to monitor environmental conditions and usage conditions of the component. For example, the environmental conditions module  204  uses data from the temperature sensor  137 , the humidity sensor  138 , the vibration sensor  135 , and/or other sensors. In some embodiments, the component is capable of being monitored and controlled. In some examples, the component is monitored to gather usage data, such as number of times the component is operated, voltage at or within the component, current through the component, and the like. In some embodiments, the condition monitoring system monitors usage conditions of the component. In some embodiments, the environmental conditions module  204  gathers data from a system different than the condition monitoring system. 
     The apparatus  200  includes a lifetime model modification module  206  configured to modify the baseline lifetime model based on the monitored environmental and usage conditions to produce a modified lifetime model for the component. For example, the lifetime model modification module  206  uses environmental and/or usage data gathered by the environmental conditions module  204  and inputs the data into one or more equations of the baseline lifetime model to derive the modified lifetime model of the component being monitored, which may result in a modified curve  604  as depicted in  FIG. 6 . 
     In some embodiments, the lifetime model modification module  206  uses a cumulative average of usage and/or environmental conditions. In another embodiment, the lifetime model modification module  206  uses a cumulative total of one or more usage and/or environmental conditions. For example, the lifetime model modification module  206  may use a cumulative total of operational cycles of the component. In some embodiments, the lifetime model modification module  206  includes weighting factors for one or more usage and environmental conditions. For example, the lifetime model modification module  206  may weight extreme temperatures beyond a threshold temperature more than less extreme temperatures. One of skill in the art will recognize other ways that the lifetime model modification module  206  uses monitored environmental and/or usage conditions to modify the baseline lifetime model to derive the modified lifetime model. 
     The apparatus  200  includes a lifetime tracking module  208  configured to track a lifetime progress of the component with respect to the modified lifetime model. In one example, the lifetime tracking module  208  tracks usage time of the component. In some embodiments, the lifetime tracking module  208  tracks only time where the component is being used, is active, is operational, or the like. In other embodiments, the lifetime tracking module  208  tracks a total time after the component is installed in a system with physical devices, such as the manufacturing line  108 . 
     In some embodiments, the lifetime model modification module  206  continually or periodically updates the baseline lifetime model while the lifetime tracking module  208  tracks the lifetime progress of the component on a most recent modified lifetime model. In another embodiment, the lifetime tracking module  208  tracks operational cycles of the component to derive lifetime progress of the component. For example, the curves  602 ,  604  in  FIG. 6  may have an x-axis of operational cycles instead of time. One of skill in the art will recognize other ways for the lifetime tracking module  208  to track a lifetime progress of the component with respect to the modified model. 
     The apparatus  200  includes an alert module  210  configure to send an alert in response to lifetime progress of the component reaching a lifetime threshold associated with the modified lifetime model. In one example, the lifetime threshold is the wear-out time 2 t w2  of the modified lifetime model. In other embodiments, the alert module  210  predicts a wear-out time t w  and sets the lifetime threshold a certain amount of time before the wear-out time t w . In some embodiments, the lifetime threshold is set to allow time for the component to be replaced, which may include simple installation time or may include ordering time, shipping time and installation time. In other embodiments, the alert module  210  sends multiple alerts based on multiple lifetime thresholds. For example, the alert module  210  may send an alert in advance of the wear-out time t w  and may send another alert at the wear-out time t w  and possibly another alert at a higher probably of failure. The alert may be sent to a plant manager, a system administrator or other person capable of acting on the alert. One of skill in the art will recognize other ways for the alert module  210  to send an alert and other applicable lifetime thresholds. 
       FIG. 3  is a schematic block diagram of another apparatus  300  for predicting end-of-life for a component according to an embodiment. The apparatus  300  includes another embodiment of the lifetime apparatus  102  with a baseline lifetime model module  202 , an environmental conditions module  204 , a lifetime model modification module  206 , a lifetime tracking module  208  and an alert module  210 , which are substantially similar to those described above in relation to the apparatus  200  of  FIG. 2 . The lifetime apparatus  102  includes a baseline lifetime model module  202  that includes a machine learning module  302 , a testing module  304  and/or an experience module  306 , which are described below. In other embodiments, one or more of the machine learning module  302 , the testing module  304  and/or the experience module  306  are located external to the baseline lifetime model module  202  and are accessible by the baseline lifetime model module  202  to determine a baseline lifetime model for the component of the system with physical devices. The apparatus  300 , in some embodiments, is implemented in a similar way as the apparatus  200  of  FIG. 2 . 
     The apparatus  300 , in some embodiments, includes a machine learning module  302  configured to use machine learning to contribute to derivation of the baseline lifetime model that is modifiable and applicable to the component of the system  100 . In some embodiments, the machine learning includes tracking a lifetime progress of a plurality of components similar to the component of the system  100  and tracking usage conditions, environmental conditions, and failures of the plurality of components to derive modifications applicable to the baseline lifetime model based on the usage conditions and applicable environmental conditions of the component of the system  100 . In one example, the machine learning module  302  accesses data from multiple systems with a component similar to the component being monitored by lifetime apparatus  102  of the system  100 . In some example, the machine learning module  302  accesses a deep neural network with inputs from various similar components and associated environmental and usage conditions and failure data of the components. The machine learning module  302 , in other examples, accesses data collected for components similar to the component being monitored by the lifetime apparatus  102 . 
     In some embodiments, the components similar to the component being monitored have a same model number, are of a same component family or are otherwise similar enough to have a same or similar lifetime characteristics that are affected in similar ways by environmental and usage conditions. The machine learning module  302  tracks failure data of the plurality of components to be able to determine how various conditions affect lifetime of the plurality of components. In some embodiments, the machine learning module  302  adjusts parameters, weighting factors, etc. of the baseline lifetime model so that over time the baseline lifetime model is more accurate. In other embodiments, adjusting parameters, weighting factors, etc. of the baseline lifetime model the machine learning module  302  refines how the baseline lifetime model is affected by various conditions. 
     In some embodiments, the machine learning module  302  contributes to the baseline lifetime model at the beginning of life of a component when the component is installed. In other embodiments, the machine learning module  302  works in conjunction with the lifetime model modification module  206  to affect modified lifetime models. One of skill in the art will recognize other implementations of the machine learning module  302  to contribute to the derivation of the baseline lifetime model and/or the modified lifetime model of a component. 
     The apparatus  300 , in some embodiments, includes a testing module  304  configured to contribute to derivation of the baseline lifetime model that is modifiable and applicable to the component of the system with physical devices by testing a plurality of components similar to the component of the system with physical devices under various usage and environmental conditions and tracking failures of the plurality of components. In some embodiments, the testing module  304  controls testing equipment used to test a plurality of components similar to the component of the system with physical devices and adjusts both environmental conditions and usage conditions of the components and determines when each component fails and then uses data from the testing to modify the baseline lifetime model. In other embodiments, the testing module  304  accesses test results for a plurality of components similar to the component of the system with physical devices where the test results are for various environmental and usage conditions and include failure data, and uses the test results for modifying the baseline lifetime model and/or a modified lifetime model of the component of the system with physical devices. One of skill in the art will recognize other ways that the testing module  304  can use test results for various environmental and usage conditions and failure data to modify a baseline lifetime model for the component of the system with physical devices. 
     The apparatus  300  includes, in some embodiments, an experience module  306  configured to contribute to derivation of the baseline lifetime model that is modifiable and applicable to the component of the system with physical devices by gathering information from customers reporting component failures of components similar to the component of the system with physical devices and gathering usage information and environmental information where the components were installed. For example, the experience module  306  may include a graphical user interface (“GUI”) for a customer or other person to input failure data and associated environmental and/or usage conditions of the components. In other embodiments, the experience module  306  accesses one or more databases of customers where component failure data and associated environmental and/or usage conditions of the similar components are stored. The experience module  306  uses the customer data to modify the baseline lifetime model and/or a modified lifetime model of the component of the system with physical devices. 
       FIG. 4  is a flowchart diagram of a method  400  for predicting end-of-life for a component according to an embodiment. The method  400  begins and determines  402  a baseline lifetime model for a component connected to a machine functional safety system  101 . The component is part of a system with physical devices, such as the manufacturing line  108 . The method  400  monitors  404  environmental conditions and usage conditions of the component and modifies  406  the baseline lifetime model based on the monitored environmental and usage conditions to produce a modified lifetime model for the component. The method  400  tracks  408  a lifetime progress of the component with respect to the modified lifetime model. 
     The method  400  determines  410  if a lifetime progress of the component reaches a lifetime threshold associated with the modified lifetime model. If the method  400  determines  410  that the lifetime progress of the component reaches a lifetime threshold associated with the modified lifetime model, the method  400  sends  412  and alert and the method  400  ends. If the method  400  determines  410  that the lifetime progress of the component has not reached a lifetime threshold associated with the modified lifetime model, the method  400  returns and monitors  404  environmental conditions and usage conditions of the component. In various embodiments, the method  400  is implemented with one or more of the baseline lifetime model module  202 , the environmental conditions module  204 , the lifetime model modification module  206 , the lifetime tracking module  208  and the alert module  210 . 
       FIG. 5  is a flowchart diagram of another method  500  for predicting end-of-life for a component according to an embodiment. The method  500  begins and, in some embodiments, tests  502  a plurality of components similar to a component of a system with physical devices under various usage and environmental conditions and tracks failures of the plurality of components to contribute to derivation of a baseline lifetime model for the component. The method  500 , in some embodiments, uses  504  information gathered from customers reporting component failures of components similar to the component of the system with physical devices and gathers usage information and environmental information where the components were installed to contribute to derivation of the baseline lifetime model for the component. 
     The method  500 , in some embodiments, uses  506  machine learning to contribute to derivation of the baseline lifetime model that is modifiable and applicable to the component of the system with physical devices. In some embodiments, the machine learning tracks a lifetime progress of a plurality of components similar to the component of the system with physical devices and tracks usage conditions, environmental conditions, and failures of the plurality of components to derive modifications applicable to the baseline lifetime model based on the usage conditions and applicable environmental conditions of the component of the system with physical devices. 
     The method  500  monitors  508  environmental conditions and usage conditions of the component and modifies  510  the baseline lifetime model based on the monitored environmental and usage conditions to produce a modified lifetime model for the component. The method  500  tracks  512  a lifetime progress of the component with respect to the modified lifetime model and determines  514  if a lifetime progress of the component reaches a lifetime threshold associated with the modified lifetime model. If the method  500  determines  514  that the lifetime progress of the component reaches a lifetime threshold associated with the modified lifetime model, the method  500  sends  516  and alert and the method  500  ends. If the method  500  determines  514  that the lifetime progress of the component has not reached a lifetime threshold associated with the modified lifetime model, the method  500  returns and monitors  508  environmental conditions and usage conditions of the component. In various embodiments, the method  500  is implemented with one or more of the baseline lifetime model module  202 , the environmental conditions module  204 , the lifetime model modification module  206 , the lifetime tracking module  208 , the alert module  210 , the machine learning module  302 , the testing module  304  and/or the experience module  306 . 
     This description uses examples to disclose the invention and also to enable any person skilled in the art to practice the invention, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the invention is defined by the claims and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements with insubstantial differences from the literal language of the claims.