Online estimation method for equipment remaining life considering dependent competing failure modes

Abstract

The present invention provides an online remaining life assessment method for equipment considering dependent competing failure modes, relating to the technical field of equipment remaining life assessment. In the present invention, a joint one-step transition probability matrix Γ(r) is constructed for age and degradation state within the unit interval (r, r+1)τ. Based on the joint one-step transition probability matrix Γ(r), Γ(r+1),…, Γ(r+s-1), the probability R(s|r,i) that the equipment is reliable at age (r+s)τ is predicted. Based on the probability R(s|r,i), the remaining life increment #imgabs0# of the equipment at age (r+s)τ is calculated, and if #imgabs1##imgabs2# is less than a preset precision error ε, the algorithm is stopped and the final #imgabs4# is output as the online remaining life assessment result for the equipment. Because the degradation process is no longer limited to the Wiener process, the more practical Markov process is adopted, which has more universal application significance. The iterative recursive algorithm evaluates the average remaining life with dependent competing soft and hard faults. At the same time, the algorithm has small memory requirements and fast calculation speed, which meets the timeliness requirements of online health status assessment and management.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment remaining life assessment, and in particular to an online assessment method, system, storage medium and electronic device for equipment remaining life considering dependent competing failure modes. Background Art

[0002] In engineering practice, equipment failures include both hard and soft failures. Hard failures are caused by equipment downtime and are related to equipment age. Soft failures occur when the equipment's degradation level exceeds a certain threshold, resulting in a malfunction. Equipment failures are the result of a competing failure between hard and soft failures. Once a hard or soft failure occurs, the equipment is considered faulty. Advances in sensor and signal processing technologies have made it easier to determine the degradation level of equipment. Therefore, assessing the remaining life of equipment based on age and degradation information is crucial for equipment health management.

[0003] Currently, to facilitate the assessment or prediction of remaining life, existing technologies often assume that the degradation process follows the Wiener process, thereby leveraging the properties of the Wiener process and normal distribution to derive health indicators such as the conditional reliability and average remaining life of the equipment. Although the Wiener process is a widely used model, it cannot describe discrete or monotonic degradation processes. If the degradation process does not follow the Wiener process, the remaining life assessment that considers age and degradation becomes more complex. Existing technologies solve for the average remaining life based on a joint transition probability matrix of age and degradation state over the entire life cycle. The discretization level of the equipment's lifespan, degradation state, quantity, and age all have a significant impact on this method. In the case of long lifespans and multiple degradation states, this method requires more computing memory, has slow computational speeds, and struggles to meet the efficiency requirements of online health management. Summary of the Invention

[0004] (1) Technical problems solved

[0005] In response to the shortcomings of the existing technology, the present invention provides an online evaluation method, system, storage medium and electronic device for the remaining life of equipment taking into account dependent and competing failure modes, which solves the technical problem of difficulty in meeting the high efficiency requirements of online health management.

[0006] (2) Technical solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0008] An online assessment method for the remaining life of equipment considering dependent competing failure modes includes:

[0009] S1. Obtain the age rτ of the device to be evaluated, where r is an integer and τ is a unit of time, and the degradation state i obtained by condition monitoring; and initialize the remaining life Let P be initialized as the identity matrix, s = 1;

[0010] S2. Construct a joint one-step transition probability matrix Γ(r+s-1) of age and degradation state within the interval (r+s-1, r+s)τ, where the transition probability refers to the probability that the device does not experience a hard failure within the above unit time and the degradation state transitions to another state;

[0011] S3. Calculate the joint transition probability matrix P = PΓ(r+s-1) within the interval (r, r+s)τ;

[0012] S4. Predict the probability R(s|r, i) that the device is reliable at age (r+s)τ based on the joint transition probability matrix P within the interval (r, r+s)τ;

[0013] S5. Calculate the reliable remaining life increment of the equipment at (r+s)τ And let

[0014] S6, if If the error is less than the preset precision ε, the algorithm stops and the final As the online evaluation result of the remaining life of the equipment; otherwise, set s=s+1 and return to S2.

[0015] Preferably, in the method:

[0016] The degradation process of the equipment is represented by the Markov model; let represents a set of degradation states, where a larger value of the degradation state indicates a higher degradation level, and I represents a soft fault state. The transition probability matrix of the degradation state within a unit time τ is as follows:

[0017]

[0018] Among them, Q i，j represents the transition probability from degraded state i to j; and Q II =1,Q Ij =0,

[0019] The failure rate function of the device itself can be a general distribution function, represented by λ0(t); the degradation process affects the occurrence time of hard failure, so the device in the degraded state is The composite failure rate function can be expressed as

[0020] λ(t, i)=λ0(t)·ψ(Z i ) (2)

[0021] where ψ(Z i) represents the degradation level Z of state i i Impact on failure rate.

[0022] Preferably, the S2 specifically includes:

[0023] S21. Based on the age rτ and degradation state i, calculate the probability that the device does not experience a hard failure within the next unit time τ and the degradation state transitions to j:

[0024]

[0025] Where ξ represents the age of the device when a hard failure occurs, Represents the degraded state of the system at time rτ, and the inequality sign approximates the expected reliability under random transitions of the degraded state;

[0026] S22. Use formula (3) to construct the joint one-step transition probability matrix of age and degradation state in the unit interval (r, r+1)τ:

[0027] Γ(r)=[Γ(r,i,j)] I×I (4).

[0028] Preferably, the S4 includes:

[0029] Constructing Λ i is an I-dimensional row vector whose i-th element is 1 and the rest of the elements are 0. represents an I-dimensional row vector with the i-th element being 0 and the remaining elements being 1; then the probability that the device is reliable at age (r+s)τ is:

[0030]

[0031] in, express The transpose of ; P represents the joint transition probability matrix within (r, r+s)τ, which is the result of multiplying Γ(n) from n=r to r+s-1, that is,

[0032] An online assessment system for the remaining life of equipment considering dependent competing failure modes comprises:

[0033] Initialization module, used to execute S1, obtain the age rτ of the device to be evaluated, where r is an integer and τ is a unit of time, and the degradation state i obtained by condition monitoring; and initialize the remaining life Let P be initialized to the I×I identity matrix, s=1;

[0034] A construction module is configured to execute S2 and construct a joint one-step transition probability matrix Γ(r+s-1) of age and degradation state within the interval (r+s-1, r+s)τ, where the transition probability refers to the probability that the device does not suffer a hard fault within the above unit time and the degradation state transitions to another state;

[0035] A first calculation module is used to execute S3 and calculate a joint transition probability matrix P=PΓ(r+s-1) within an interval (r, r+s)τ;

[0036] A prediction module, configured to execute S4, predict a probability R(s|r, i) that the device is reliable at age (r+s)τ based on a joint transition probability matrix P within an interval (r, r+s)τ;

[0037] The second calculation module is used to execute S5 and calculate the reliable remaining life increment of the device at (r+s)τ And let

[0038] Output module, used to execute S6, If the error is less than the preset precision ε, the algorithm stops and the final As the online evaluation result of the remaining life of the device; otherwise, set s=s+1 and return to the construction module to execute S2.

[0039] A storage medium stores a computer program for online evaluation of the remaining service life of a device taking into account dependent competing failure modes, wherein the computer program enables a computer to execute the above-mentioned online evaluation method for the remaining service life of a device.

[0040] An electronic device, comprising:

[0041] one or more processors;

[0042] Memory; and

[0043] One or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs including instructions for executing the above-mentioned method for online assessment of remaining life of a device.

[0044] (3) Beneficial effects

[0045] The present invention provides a method, system, storage medium, and electronic device for online evaluation of the remaining useful life of a device that considers dependent competing failure modes. Compared with existing technologies, this method has the following advantages:

[0046] In the present invention, the age rτ of the device to be evaluated and the degradation state i obtained by state monitoring are obtained; and the remaining life is initialized Let s = 1; construct a joint one-step transition probability matrix Γ(r+s-1) of age and degradation state in any unit interval (r+s-1, r+s)τ, where s = 1, 2, ...; predict the probability R(s|r, i) that the device is reliable at the age (r+s)τ based on the joint one-step transition probability matrix Γ(r), Γ(r+1), ..., Γ(r+s-1); calculate the remaining life increment of the device under the reliable condition of the age (r+s)τ based on the probability R(s|r, i) And let like If the error is less than the preset precision ε, the algorithm stops and the final This is used as the online remaining lifetime assessment result for the device; otherwise, s is set to s + 1 and the iterative process continues. By overcoming the limitation of the degradation process being subject to the Wiener process and adopting a more practical Markov process, it has broader application implications. The iterative algorithm estimates the average remaining lifetime of dependent competing soft and hard faults. Furthermore, the algorithm requires minimal memory and offers fast computational speed, meeting the timeliness requirements of online health status assessment and management. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0048] Figure 1 A flow chart of an online method for evaluating the remaining useful life of equipment considering dependent competing failure modes provided by an embodiment of the present invention;

[0049] Figure 2 A schematic diagram of an expected remaining life calculation result provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0050] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0051] The embodiments of the present application solve the technical problem of difficulty in meeting the efficiency requirements of online health management by providing an online evaluation method, system, storage medium and electronic device for the remaining life of a device taking into account dependent competing failure modes.

[0052] The technical solution in the embodiments of the present application is to solve the above technical problems, and the overall idea is as follows:

[0053] First, the embodiments of the present invention overcome the limitation of the degradation process being subject to the Wiener process and instead employ the more practical Markov process. It should be noted that various continuous-state degradation processes, such as the gamma process, the Wiener process, and the inverse Gaussian process, can be converted into Markov processes through appropriate discretization methods. Therefore, the present invention has broader applicability.

[0054] Secondly, the present invention proposes an iterative recursive algorithm to evaluate the average remaining life of dependent competing soft and hard faults. The algorithm also requires low memory requirements and high computation speed to meet the timeliness requirements of online health status assessment and management.

[0055] Specifically, the present invention includes: obtaining the age rτ of the device to be evaluated and the degradation state i obtained by state monitoring; and initializing the remaining life Let P be an I×I identity matrix, s=1; construct a joint one-step transition probability matrix Γ(r+s-1) of age and degradation state within the interval (r+s-1, r+s)τ, where the transition probability refers to the probability that the device does not suffer a hard failure and the degradation state transitions to another state within the above unit time; calculate the joint transition probability matrix P=PΓ(r+s-1) within the interval (r, r+s)τ; predict the probability R(s|r, i) that the device is reliable at age (r+s)τ based on the joint transition probability matrix P within the interval (r, r+s)τ; calculate the reliable remaining life increment of the device at (r+s-1, r+s)τ. R(s|r,i), and let like If the error is less than the preset precision ε, the algorithm stops and the final As the online evaluation result of the remaining life of the equipment; otherwise, set s=s+1 and continue the iterative process.

[0056] The algorithm proposed in the embodiment of the present invention greatly reduces the demand for computing memory and improves computing speed while ensuring computing accuracy.

[0057] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0058] Example:

[0059] like Figure 1 As shown, an embodiment of the present invention provides an online method for evaluating the remaining service life of a device considering dependent competing failure modes, including:

[0060] S1. Obtain the age rτ of the device to be evaluated, where r is an integer and τ is a unit of time, and the degradation state i obtained by condition monitoring; and initialize the remaining life Let P be initialized as the identity matrix, s = 1;

[0061] S2. Construct a joint one-step transition probability matrix Γ(r+s-1) of age and degradation state within the interval (r+s-1, r+s)τ, where the transition probability refers to the probability that the device does not experience a hard failure within the above unit time and the degradation state transitions to another state;

[0062] S3. Calculate the joint transition probability matrix P = PΓ(r+s-1) within the interval (r, r+s)τ;

[0063] S4. Predict the probability R(s|r, i) that the device is reliable at age (r+s)τ based on the joint transition probability matrix P within the interval (r, r+s)τ;

[0064] S5. Calculate the reliable remaining life increment of the equipment at (r+s)τ And let

[0065] S6, if If the error is less than the preset precision ε, the algorithm stops and the final As the online evaluation result of the remaining life of the equipment; otherwise, set s=s+1 and return to S2.

[0066] This embodiment of the present invention overcomes the limitation of the degradation process being subject to the Wiener process and adopts the more practical Markov process, thus having wider application significance. The iterative recursive algorithm estimates the average remaining lifetime of dependent competing soft and hard faults. Furthermore, the algorithm has low memory requirements and fast computation speed, meeting the timeliness requirements of online health status assessment and management.

[0067] The following will introduce each step of the above technical solution in detail with specific content:

[0068] First, it should be noted that the technical solution provided by this invention involves a relatively general RUL estimation algorithm with very broad adaptability. First, neither the hard faults nor the degradation processes in this model are limited to a single distribution or model. Second, the algorithm is applicable to cases where hard and soft faults are independent. Finally, the algorithm is equally applicable to RUL estimation when only hard faults or only soft faults are considered.

[0069] In particular, the present invention overcomes the limitation of the degradation process being subject to the Wiener process and adopts the more practical Markov process. It should be noted that various continuous-state degradation processes, such as the gamma process, the Wiener process, and the inverse Gaussian process, can be converted into Markov processes through appropriate discretization methods. Therefore, this method has more universal application significance.

[0070] In step S1, the age rτ of the device to be evaluated is obtained, where r is an integer and τ is a unit time, and the degradation state i obtained by condition monitoring; and the remaining life is initialized Let P be initialized to an I×I identity matrix, s=1.

[0071] The degradation process of the equipment is represented by the Markov model; let represents a set of degradation states, where a larger degradation state value indicates a higher degradation level, and I represents a soft fault state. The embodiments of the present invention are based on state transitions and fault patterns per unit time. Therefore, the transition probability matrix of the degradation state per unit time is first given as follows:

[0072]

[0073] Among them, Q i，j represents the transition probability from degraded state i to j; here, the degradation process is not restricted to a strictly increasing process. However, once a soft fault occurs, it is no longer possible to transition to other states, i.e., Q II =1,Q Ij =0,

[0074] The failure rate function of the device itself can be a general distribution function, represented by λ0(t); the degradation process affects the occurrence time of hard failure, so the device in the degraded state is The composite failure rate function can be expressed as

[0075] λ(t, i)=λ0(t)·ψ(Z i ) (2)

[0076] where ψ(Z i ) represents the degradation level Z of state i i Impact on failure rate.

[0077] In step S2, a joint one-step transition probability matrix Γ(r) of age and degradation state in the interval (r, r+1) is constructed. The transition probability refers to the probability that no hard failure occurs in the device within the next unit time and the degradation state transitions to j; specifically, it includes:

[0078] S21. Based on the age r and degradation state i, calculate the probability that the device does not experience a hard failure and the degradation state transitions to j within the next unit time:

[0079]

[0080] Where ξ represents the age of the device when a hard failure occurs, represents the degraded state of the system at time rτ, and the inequality sign approximately calculates the expected reliability under random transitions of the degraded state; Formula (3) takes into account the upper and lower limits of the reliability, which improves the accuracy and robustness of the algorithm.

[0081] S22. Use formula (3) to construct the joint one-step transition probability matrix of age and degradation state in the interval (r, r+1)τ:

[0082] Γ(r)=[Γ(r,i,j)] I×I (4)

[0083] In step S3 , a joint transition probability matrix P=PΓ(r+s−1) within the interval (r, r+s)τ is calculated.

[0084] In step S4, based on the joint one-step transition probability matrix Γ(r), the probability R(s|r, i) that the device is reliable at age r+s is predicted. Specifically, the method includes:

[0085] Constructing Λ i is an I-dimensional row vector whose i-th element is 1 and the rest of the elements are 0. represents an I-dimensional row vector whose i-th element is 0 and the rest are 1. By recursion, the following rule exists: if the degradation state of a device at age r is i, then the probability that the device will be reliable at age r+s is:

[0086]

[0087] in, express The transpose of ; P represents the joint transition probability matrix within (r, r+s)τ, which is the result of multiplying Γ(n) from n=r to r+s-1, that is, In the algorithm, S3 is used to solve P. Formula (5) greatly facilitates program writing.

[0088] In step S5, the reliable remaining life increment of the device at the lifetime (r+s)τ is calculated according to the probability R(s|r,i) And let

[0089] The remaining life increment in this step The specific solution process includes:

[0090]

[0091] Based on the idea of ​​recursion, the recursive formula of the remaining life is constructed. This recursive formula avoids the creation of a huge full-life cycle matrix, greatly reduces memory requirements, and improves computing speed.

[0092] In step S6, if If the error is less than the preset precision ε, the algorithm stops and the final As the online evaluation result of the remaining life of the equipment; otherwise, set s=s+1 and return to S2.

[0093] In order to better illustrate the technical effect of the method for online assessment of the remaining life of equipment provided by the embodiment of the present invention, a specific application example is further provided as follows:

[0094] The degradation process of a machine tool feed system follows a gamma process. For ease of calculation, it is discretized into 41 states with a discretization level of 0.25. The range of state i∈{1,…,40} is [0.25i, 0.25(i+1)), and the range of state 41 is (10.25, +∞). The degradation level of state i is 0.25(i-0.5). The unit time is 5, and the transition probability of each degradation state is:

[0095]

[0096] Among them, l b (i, j) = max (0, 0.25 (ji-0.5)), if j ≠ 41, u b (i,j)=0.25(j-i+0.5); if j=41, u b (i, j) = ∞.

[0097] The hard failure process of the feed system obeys the Weibull proportional hazard model as follows:

[0098]

[0099] Among them, α=2050, β=4.63, and γ=0.281.

[0100] If the equipment age is 30, the equipment is fault-free and the degradation state obtained by condition monitoring is 2, the expected remaining life of the equipment is to be estimated. Assuming ε = 1, the algorithm stops after the 519th iteration and calculates the expected remaining life to be 1558.9, the calculation time is 22 seconds, and the required memory is 30.7Mb. The calculation results are as follows Figure 2 shown.

[0101] Therefore, it is clear that the embodiments of the present invention significantly reduce the need for computing memory and improve computing speed while ensuring computational accuracy. The memory usage and computation time of the above application examples show that the algorithm's computation time is approximately 10% of that of the full lifecycle transfer matrix method, while requiring less than 1% of the memory required, significantly improving online evaluation capabilities.

[0102] An embodiment of the present invention provides an online system for evaluating the remaining useful life of equipment considering dependent competing failure modes, including:

[0103] Initialization module, used to execute S1, obtain the age rτ of the device to be evaluated, where r is an integer and τ is a unit of time, and the degradation state i obtained by condition monitoring; and initialize the remaining life Let P be initialized to the I×I identity matrix, s=1;

[0104] A construction module is configured to execute S2 and construct a joint one-step transition probability matrix Γ(r+s-1) of age and degradation state within the interval (r+s-1, r+s)τ, where the transition probability refers to the probability that the device does not suffer a hard fault within the above unit time and the degradation state transitions to another state;

[0105] A first calculation module is used to execute S3 and calculate a joint transition probability matrix P=PΓ(r+s-1) within an interval (r, r+s)τ;

[0106] A prediction module, configured to execute S4, predict a probability R(s|r, i) that the device is reliable at age (r+s)τ based on a joint transition probability matrix P within an interval (r, r+s)τ;

[0107] The second calculation module is used to execute S5 and calculate the reliable remaining life increment of the device at (r+s)τ And let

[0108] Output module, used to execute S6, If the error is less than the preset precision ε, the algorithm stops and the final As the online evaluation result of the remaining life of the device; otherwise, set s=s+1 and return to the construction module to execute S2.

[0109] An embodiment of the present invention provides a storage medium storing a computer program for online evaluation of the remaining service life of a device considering dependent competing failure modes, wherein the computer program enables a computer to execute the above-described online evaluation method for the remaining service life of a device.

[0110] An embodiment of the present invention further provides an electronic device, including:

[0111] one or more processors;

[0112] Memory; and

[0113] One or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs including instructions for executing the above-mentioned method for online assessment of remaining life of a device.

[0114] It can be understood that the online evaluation system for the remaining life of a device considering dependent and competing failure modes, storage medium and electronic device provided in the embodiments of the present invention correspond to the online evaluation method for the remaining life of a device considering dependent and competing failure modes provided in the embodiments of the present invention. The explanation, examples and beneficial effects of the relevant contents can refer to the corresponding parts in the online evaluation method for the remaining life of the device, and will not be repeated here.

[0115] In summary, compared with the existing technology, the present invention has the following beneficial effects:

[0116] This embodiment of the present invention overcomes the limitation of the degradation process being subject to the Wiener process and adopts the more practical Markov process, thus having wider application significance. The iterative recursive algorithm estimates the average remaining lifetime of dependent competing soft and hard faults. Furthermore, the algorithm has low memory requirements and fast computation speed, meeting the timeliness requirements of online health status assessment and management.

[0117] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0118] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. An online evaluation method for the remaining life of equipment considering dependent competing failure modes, characterized by: include: S1. Obtain the age rτ of the equipment to be evaluated, where r is an integer and τ is a unit of time, and the degradation state i obtained by condition monitoring; And initialize the remaining life Let P be initialized as the identity matrix, s = 1; S2. Construct a joint one-step transition probability matrix Γ(r+s-1) of age and degradation state within the interval (r+s-1, r+s)τ, where the transition probability refers to the probability that the device does not experience a hard failure and the degradation state transitions to another state within the above unit time; S3. Calculate the joint transition probability matrix P = PГ(r+s-1) within the interval (r, r+s)τ; S4. Based on the joint transition probability matrix P within the interval (r, r+s)τ, predict the probability R(s|r,i) that the device is reliable at age (r+s)τ; s5. Calculate the reliable remaining life increment of the equipment at (r+s)τ And let S6, if If the error is less than the preset precision ε, the algorithm stops and the final As the online evaluation result of the remaining life of the equipment; otherwise, set s = s + 1 and return to S2; The degradation process of the equipment is represented by the Markov model; let represents a set of degradation states, where a larger value of the degradation state indicates a higher degradation level, and I represents a soft fault state. The transition probability matrix of the degradation state within a unit time τ is as follows: Among them, Qi, j represents the transition probability from degraded state i to j; and The failure rate function of the device itself can be a distribution function, represented by λ0(t); the degradation process affects the occurrence time of hard failure, so the device in the degraded state is The composite failure rate function can be expressed as λ(t,i)=λ0(t)·ψ(Z i ) (2) where ψ(Z i ) represents the degradation level Z of state i i Impact on failure rate; The S2 specifically includes: S21. Based on the age rτ and degradation state i, calculate the probability that the device does not experience a hard failure within the next unit time τ and the degradation state transitions to j: Where ξ represents the age of the device when a hard failure occurs, Represents the degraded state of the system at time rτ, and the inequality sign approximates the expected reliability under random transitions of the degraded state; S22. Use formula (3) to construct the joint one-step transition probability matrix of age and degradation state in the unit interval (r, r+1)τ: G(r)=[G(r,i,j)] I×I (4).

2. The method for online assessment of remaining equipment life according to claim 1, wherein: The S4 includes: Build A i is an I-dimensional row vector whose i-th element is 1 and the rest of the elements are 0. represents an I-dimensional row vector with the i-th element being 0 and the remaining elements being 1; then the probability that the device is reliable at age (r+s)τ is: in, express The transpose of ; P represents the joint transition probability matrix within (r, r+s)τ, which is the result of multiplying Г(n) from n=r to r+s-1, that is, 3. An online assessment system for the remaining life of equipment considering dependent competing failure modes, characterized by: The method for performing the online remaining life assessment of equipment according to claim 1 comprises: Initialization module, used to execute S1, obtain the age rτ of the device to be evaluated, where r is an integer and τ is a unit of time, and the degradation state i obtained by condition monitoring; and initialize the remaining life Let P be initialized as the identity matrix, s = 1; A construction module is configured to execute S2 and construct a joint one-step transition probability matrix Г(r+s-1) of age and degradation state within the interval (r+s-1, r+s)τ, where the transition probability refers to the probability that the device does not suffer a hard fault within the above unit time and the degradation state transitions to another state; A first calculation module is used to execute S3 and calculate a joint transition probability matrix P=PГ(r+s-1) within an interval (r, r+s)τ; A prediction module, configured to execute S4, predict the probability R(s|r,i) that the device is reliable at age (r+s)τ based on a joint transition probability matrix P within the interval (r, r+s)τ; The second calculation module is used to execute S5 and calculate the reliable remaining life increment of the device at (r+s)τ And let Output module, used to execute S6, If the error is less than the preset precision ε, the algorithm stops and the final As the online evaluation result of the remaining life of the device; otherwise, set s=s+1 and return to the construction module to execute S2.

4. A storage medium, characterized in that The computer program is stored therein for online evaluation of the remaining service life of a device taking into account dependent competing failure modes, wherein the computer program enables a computer to execute the online evaluation method of the remaining service life of a device as claimed in claim 1 or 2.

5. An electronic device, characterized in that: include: one or more processors; Memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs including instructions for executing the method for online assessment of the remaining life of the device as claimed in claim 1 or 2.

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