Intelligent temperature controller durability test system

Abstract

The invention relates to the technical field of equipment testing, in particular to an intelligent temperature controller durability testing system, which comprises a state modeling unit, a state analysis unit, a state analysis unit and a state analysis unit, and defining a state transition condition of the target temperature controller according to the extended variable set and the state set, and constructing a state transition diagram according to the extended variable set, the state set and the state transition condition. By defining the state set, the extended variable set and the state transition condition of the target temperature controller, the behavior and state change of the temperature controller under different working conditions can be accurately simulated and described, and by constructing the state transition diagram, the test case can be ensured to cover all working states of the temperature controller, so that the test efficiency is improved. And the system is comprehensively tested, so that the reliability of the equipment is better evaluated.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment testing, and particularly relates to an intelligent thermostat durability testing system. Background Art

[0002] A thermostat is a device that automatically adjusts temperature and is commonly used to keep the temperature within a preset range.

[0003] Traditional systems do not systematically model the state of the thermostat. They often rely on experience or fixed test cases and do not have a comprehensive state transition diagram. The test coverage is limited, and some specific working states or boundary conditions may be overlooked, resulting in incomplete testing and a risk of missed testing. Moreover, traditional systems generally rely on predefined test scripts or static test paths, lacking flexible exploration of complex state spaces. The test paths are usually predefined and do not adapt to dynamic changes in different working environments. Additionally, due to traditional systems usually being based on limited manual test paths, their test coverage and efficiency are low, especially when facing complex working environments or large-scale devices, it is difficult to ensure comprehensive test coverage. Also, traditional systems require manual intervention to generate test cases, execute tests, and perform data analysis. During the testing process, manually writing scripts, designing test cases, etc. all consume a large amount of manpower. And traditional testing systems usually do not optimize real-time monitoring and anomaly detection during the testing process. Although some sensors can be used to monitor the performance of the thermostat, the monitoring range and response speed are often insufficient to detect small anomalies of the device in a timely manner. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to overcome the above-mentioned disadvantages of the prior art and provide an intelligent thermostat durability testing system.

[0005] The technical solution adopted to solve the above technical problem is: an intelligent thermostat durability testing system, comprising:

[0006] A state modeling unit, which is used to define a state set and an extended variable set of a target thermostat, define state transition conditions of the target thermostat according to the extended variable set and the state set, and construct a state transition diagram according to the extended variable set, the state set, and the state transition conditions;

[0007] A state matching unit, which is used to generate a sequence of random extended variable sets, and match the sequence of random extended variable sets with the state transition diagram according to the depth-first search algorithm to obtain state transition paths corresponding to each random extended variable set in the sequence of random extended variable sets;

[0008] A use case generation unit, which is used to generate test cases according to the set of random expansion variables and the state transition path, convert the test cases into script instructions, and control the target thermostat according to the sequence of the script instructions;

[0009] A test determination unit, which is used to monitor the temperature of the temperature control environment controlled by the target thermostat to obtain the real-time temperature of the temperature control environment, and determine the test label of the target thermostat according to the test case and the real-time temperature, wherein the test label includes an abnormal label and a normal label.

[0010] Preferably, the system further includes:

[0011] An abnormal determination unit, which is used to count the test labels of the target thermostat within a preset test period. If the number of abnormal labels is greater than a preset threshold, it is determined that the target thermostat is abnormal;

[0012] A durability prediction unit, which is used to count the response time series of the target thermostat within a preset test period if the number of abnormal labels is not greater than a preset threshold, and predict the durability of the target thermostat according to the response time series.

[0013] Preferably, the state set includes a standby state, a heating state, a cooling state, an over-temperature warning state, a low-temperature warning state, and a fault state; the expansion variable set includes the current temperature, the target temperature, the maximum safe temperature, the minimum safe temperature, the used time, the temperature change rate, and the maximum safe available time; the test case includes the state transition path of the target thermostat and the set of random expansion variables before each state transition of the target thermostat.

[0014] Preferably, the state transition conditions of the target thermostat are defined according to the expansion variable set and the state set, including:

[0015] If the current temperature reaches the target temperature and the temperature change rate becomes zero, the target thermostat enters the standby state;

[0016] If the current temperature is less than the target temperature, the target thermostat enters the heating state;

[0017] If the current temperature is higher than the target temperature, the target thermostat enters the cooling state;

[0018] If the current temperature is higher than the maximum safe temperature, the target thermostat enters the over-temperature warning state;

[0019] If the current temperature is less than the minimum safe temperature, the target thermostat enters the low-temperature warning state;

[0020] If the used time is higher than the maximum safe available time, the target thermostat enters the fault state.

[0021] Preferably, a state transition diagram is constructed according to the extended variable set, the state set and the state transition conditions, including:

[0022] Each state in the state set is used as a state node of the state transition diagram;

[0023] For each state node in the state transition diagram, according to the combination of the extended variable set and the state transition conditions, it is judged whether there is a transfer path that meets the state transition conditions. If there is a transfer path that meets the state transition conditions, a transfer edge is added to the state transition diagram, and the transfer edge represents the transfer from the current state to the next state;

[0024] On each transfer edge in the state transition diagram, mark its corresponding extended variable value and state transition conditions.

[0025] Preferably, converting the test case into a script instruction includes:

[0026] Judge the signal type required by the test case according to the random extended variable set in the test case. Among them, the signal type includes a step signal and a ramp signal. When the gap between the current temperature and the target temperature is greater than a preset first gap threshold, a step signal is used, otherwise, a ramp signal is used;

[0027] Encode the signal type required by the test case into a script instruction.

[0028] Preferably, determining the test label of the target thermostat according to the test case and the real-time temperature includes:

[0029] Compare the target temperature required in the test case with the real-time temperature within a preset control period;

[0030] If the gap between the target temperature required in the test case and the real-time temperature is greater than a preset second gap threshold, it is determined that the test label of the target thermostat is an abnormal label, otherwise, it is determined that the test label of the target thermostat is a normal label.

[0031] Preferably, within a preset test period, the response time series of the target thermostat is statistically analyzed, including:

[0032] Statistically analyze the response time of the target thermostat from the current temperature to the target temperature within a preset control period for the test case;

[0033] Repeat the processing of all test cases within a preset test period to obtain the response time series of the target thermostat.

[0034] Preferably, durability prediction of the target thermostat is performed based on the response time series, including:

[0035] Define the response time degradation characteristic quantity of the target thermostat according to the response time series;

[0036] Define the relative increment of the response time degradation characteristic quantity;

[0037] Calculate the drift parameter and diffusion coefficient of the relative increment of the response time degradation characteristic quantity based on maximum likelihood estimation;

[0038] Construct the remaining durability probability density function of the target thermostat, substitute the drift parameter and diffusion coefficient of the relative increment of the response time degradation characteristic quantity into the remaining durability probability density function, and use the time corresponding to the maximum value of the remaining durability probability density function as the remaining service durability of the target thermostat.

[0039] Preferably, the expression of the response time degradation characteristic quantity is as follows:

[0040] X(t) = a + μt + σ B B(t);

[0041] Wherein, X(t) represents the response time degradation characteristic quantity, a represents the initial value of the response time, μ represents the drift coefficient, σ B represents the diffusion coefficient, B(t) represents the standard Wiener process, and t represents the response time series;

[0042] The expression of the relative increment is as follows:

[0043] ΔX(t i ) = a + μt i + σ B ΔB(t i );

[0044] Wherein, ΔX(t i ) represents the relative increment of the response time degradation characteristic quantity, t i represents the i-th response time in the response time series, and ΔB(t i ) represents the relative increment of the standard Wiener process;

[0045] The expression of the remaining durability probability density function is as follows:

[0046]

[0047] where f(t|μ,σ B , h) represents the remaining durability probability density function, and h represents the failure threshold.

[0048] The beneficial effects of the present invention are as follows: (1) By defining the state set, extended variable set, and state transition conditions of the target thermostat, the present invention can accurately simulate and describe the behavior and state changes of the thermostat under different working conditions. This modeling method provides a detailed theoretical basis for the generation of subsequent test cases and the performance evaluation of the system. By constructing a state transition diagram, it can ensure that the test cases cover all working states of the thermostat and comprehensively test the system, thereby better evaluating the reliability of the device; (2) By using the depth-first search algorithm, the present invention generates a sequence of random extended variable sets that match the state transition diagram, which makes the generation of test paths more flexible, can cope with various complex working environments and state transitions, and avoids possible test omissions in traditional test methods. Therefore, it can effectively explore in a complex state space, generate more diverse and comprehensive test cases, thereby improving the test coverage of the system. By analyzing the response time series, it can dynamically predict the durability of the thermostat during the test. The response time is an important indicator of the device performance. Timely grasping the change trend of the device's response time can effectively predict the long-term performance of the device, identify potential degradation or failures in advance, and perform corresponding maintenance or optimization; (3) By combining the random extended variable set and the state transition path to generate test cases and converting them into script instructions to automatically execute the test, this automated test can reduce manual intervention, improve test efficiency, and ensure the consistency and repeatability of the test process. By real-time monitoring the temperature of the temperature control environment, it can obtain the performance of the thermostat in real-time during its operation and determine whether there is an abnormality by comparing with the predetermined test cases. Real-time abnormality monitoring helps to detect problems in a timely manner and adjust or repair the device to avoid failures during actual use. By counting the number of abnormal labels within the test cycle, it can quickly evaluate whether there are abnormal problems with the device after the test cycle ends. If the number of abnormalities exceeds the preset threshold, the system will determine that the target thermostat is faulty. This determination mechanism can efficiently detect potential failures and handle them in a timely manner, avoiding serious failures during the formal use of the device. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of the system architecture of the overall system in an embodiment proposed by the present invention.

[0050] Reference numerals: 1, state modeling unit; 2, state matching unit; 3, use case generation unit; 4, test determination unit; 5, anomaly determination unit; 6, durability prediction unit. Detailed implementation mode

[0051] Embodiment 1, as Figure 1 shown, an intelligent thermostat durability test system proposed by the present invention includes:

[0052] A state modeling unit 1, which is used to define a state set and an extended variable set of the target thermostat, define state transition conditions of the target thermostat according to the extended variable set and the state set, and construct a state transition diagram according to the extended variable set, the state set and the state transition conditions;

[0053] A state matching unit 2, which is used to generate a sequence of random extended variable sets, and match the sequence of random extended variable sets with the state transition diagram according to the depth-first search algorithm to obtain the state transition paths corresponding to the respective random extended variable sets in the sequence of random extended variable sets;

[0054] A use case generation unit 3, which is used to generate test cases according to the random extended variable set and the state transition path, convert the test cases into script instructions, and control the target thermostat according to the sequence of script instructions;

[0055] A test determination unit 4, and an anomaly determination unit 4 is used to monitor the temperature of the temperature control environment controlled by the target thermostat to obtain the real-time temperature of the temperature control environment, and determine the test label of the target thermostat according to the test case and the real-time temperature, wherein the test label includes an anomaly label and a normal label.

[0056] In the present invention, a thermostat is a device that automatically adjusts temperature and is commonly used to keep the temperature within a preset range; a state transition diagram is a graphical representation that shows the transition relationships between different states and their triggering conditions, which is usually used to describe the behavior of a complex system; a sequence of sets of random expansion variables is a sequence of expansion variables generated according to a certain rule or randomly, and this sequence contains a series of sets of expansion variables, representing a series of operating conditions that a thermostat may encounter; the depth - first search algorithm (DFS) is an algorithm for traversing or searching a graph. Here, DFS is used to find a path in the state transition diagram, that is, according to the given sequence of sets of random expansion variables, to find the corresponding state transition path; a test case is a specific set of operation steps and expected results used to verify the function of the target thermostat, which usually includes input conditions, execution steps, and expected outputs. The test case generation unit designs test cases based on the generated sets of random expansion variables and the corresponding state transition paths; script instructions are a set of commands that can be automatically executed, usually executed by a test tool, and these instructions control the target thermostat, simulate its operation and observe the response; a test case script is to convert a test case into code or commands that can be automatically executed, and these code instructions control the thermostat to operate and record its reaction.

[0057] In an alternative embodiment, the system further includes:

[0058] Anomaly determination unit 5, which is used to count the test labels of the target thermostat within a preset test period. If the number of test labels that are anomaly labels is greater than a preset threshold, it is determined that the target thermostat has an anomaly;

[0059] Durability prediction unit 6, which is used to, if the number of test labels that are anomaly labels is not greater than a preset threshold, count the response time series of the target thermostat within a preset test period, and perform durability prediction on the target thermostat according to the response time series.

[0060] Embodiment 2, an intelligent thermostat durability test system proposed by the present invention. Compared with Embodiment 1, this embodiment further includes: The state set includes a standby state, a heating state, a cooling state, an over - temperature warning state, a low - temperature warning state, and a fault state; the set of expansion variables includes the current temperature, the target temperature, the maximum safe temperature, the minimum safe temperature, the elapsed time, the temperature change rate, and the maximum safe available time; the test case includes the state transition path of the target thermostat and the set of random expansion variables before each state transition of the target thermostat.

[0061] In an alternative embodiment, the state transition conditions of the target thermostat are defined according to the set of expansion variables and the state set, including:

[0062] If the current temperature reaches the target temperature and the temperature change rate becomes zero, the target thermostat enters the standby state;

[0063] If the current temperature is less than the target temperature, the target thermostat enters the heating state;

[0064] If the current temperature is higher than the target temperature, the target thermostat enters the cooling state;

[0065] If the current temperature is higher than the maximum safe temperature, the target thermostat enters the over-temperature warning state;

[0066] If the current temperature is less than the minimum safe temperature, the target thermostat enters the low-temperature warning state;

[0067] If the elapsed time is higher than the maximum safe available time, the target thermostat enters the fault state.

[0068] In an alternative embodiment, a state transition diagram is constructed based on the extended variable set, the state set, and the state transition conditions, including:

[0069] Each state in the state set is used as a state node of the state transition diagram;

[0070] For each state node in the state transition diagram, according to the combination of the extended variable set and the state transition conditions, it is judged whether there is a transfer path that meets the state transition conditions. If there is a transfer path that meets the state transition conditions, a transfer edge is added to the state transition diagram, and the transfer edge represents the transfer from the current state to the next state;

[0071] On each transfer edge in the state transition diagram, mark its corresponding extended variable value and state transition conditions.

[0072] It should be noted that the combination of state transition conditions refers to multiple extended variables or system conditions combined to form a composite transfer condition, which is usually to cope with more complex system behaviors and transfer rules in different scenarios.

[0073] In an alternative embodiment, converting the test case into a script instruction includes:

[0074] Judging the signal type required by the test case according to the random extended variable set in the test case. Among them, the signal type includes a step signal and a ramp signal. When the gap between the current temperature and the target temperature is greater than a preset first gap threshold, a step signal is adopted, otherwise, a ramp signal is adopted;

[0075] Encoding the signal type required by the test case into a script instruction.

[0076] It should be noted that a step signal usually refers to a sharp change in a signal, that is, instantaneously jumping from one value to another, and it is usually used to simulate a rapid state transition; a ramp signal refers to a smooth change in the signal value, usually increasing or decreasing gradually, linearly or non-linearly, and such a signal is used to simulate a steady, gradual change.

[0077] In an optional embodiment, determining the test label of the target thermostat according to the test case and the real-time temperature includes:

[0078] Comparing the target temperature required in the test case with the real-time temperature within a preset control period;

[0079] If the gap between the target temperature required in the test case and the real-time temperature is greater than a preset second gap threshold, it is determined that the test label of the target thermostat is an abnormal label, otherwise, it is determined that the test label of the target thermostat is a normal label.

[0080] In an optional embodiment, counting the response time series of the target thermostat within a preset test period includes:

[0081] Counting the response time of the target thermostat from the current temperature to the target temperature in the test case within a preset control period;

[0082] Repeatedly processing all test cases within a preset test period to obtain the response time series of the target thermostat.

[0083] It should be noted that the control period refers to a time period within which the system (such as a thermostat) performs a complete operation or adjustment. In a temperature control system, the control period can be the time interval between each measurement and adjustment of the temperature; the test period refers to the time range set during the test to observe and record the performance of the system, and within this period, the response behavior of the test system (such as a thermostat) is observed.

[0084] In an optional embodiment, predicting the durability of the target thermostat according to the response time series includes:

[0085] Defining the response time degradation characteristic quantity of the target thermostat according to the response time series;

[0086] Defining the relative increment of the response time degradation characteristic quantity;

[0087] Calculating the drift parameter and diffusion coefficient of the relative increment of the response time degradation characteristic quantity based on maximum likelihood estimation;

[0088] Construct the remaining durability probability density function of the target thermostat. Substitute the drift parameter and diffusion coefficient that reflect the relative increment of the reaction time degradation characteristic quantity into the remaining durability probability density function. Based on the time corresponding to the maximum value of the remaining durability probability density function, it is used as the remaining service durability of the target thermostat.

[0089] It should be noted that the reaction time degradation characteristic quantity is a quantity that describes the change of the thermostat's reaction time with the service time or workload. As time goes by, the performance of the device may degrade, resulting in a gradual increase in the reaction time. Therefore, the reaction time degradation characteristic quantity is used to quantify this degradation process; the relative increment of the reaction time degradation characteristic quantity refers to the proportional change between the current reaction time and the initial reaction time, which is used to quantify the degree of increase in the reaction time; maximum likelihood estimation is a statistical method that estimates the values of model parameters by maximizing the likelihood function of the data under a certain assumed model. In this context, maximum likelihood estimation is used to estimate the drift parameter and diffusion coefficient of the relative increment of the reaction time degradation characteristic quantity; the drift parameter is a parameter that describes the long-term change trend of a random process or degradation process. In the reaction time degradation model, the drift parameter reflects the average change rate of the thermostat's reaction time over time; the diffusion coefficient is a parameter that describes the uncertainty or fluctuation degree of a random process; the remaining durability probability density function describes the probability distribution of the remaining service life of a device that can continue to work normally after the current moment, which indicates the probability that the device may continue to work until a certain moment at a certain moment; the remaining service durability refers to the time that the device can continue to work normally after the current moment, which is usually related to the device's degradation process, performance degradation rate, and uncertainty.

[0090] In an optional embodiment, the expression of the reaction time degradation characteristic quantity is as follows:

[0091] X(t) = a + μt + σ B B(t);

[0092] where X(t) represents the reaction time degradation characteristic quantity, a represents the initial value of the reaction time, μ represents the drift coefficient, σ B represents the diffusion coefficient, B(t) represents the standard Wiener process, and t represents the reaction time series;

[0093] The relative increment expression is as follows:

[0094] ΔX(t i ) = a + μt i + σ B ΔB(t i );

[0095] where ΔX(t i ) represents the relative increment of the reaction time degradation characteristic quantity, ti represents the i-th reaction time in the reaction time series, ΔB(t i ) represents the relative increment of the standard Wiener process;

[0096] The expression of the remaining durability probability density function is as follows:

[0097]

[0098] where f(t|μ,σ B ,h) represents the remaining durability probability density function, and h represents the failure threshold.

[0099] The embodiments of the present invention have been described in detail above in conjunction with the accompanying drawings. However, the present invention is not limited thereto. Various changes can be made within the scope of knowledge possessed by those skilled in the art without departing from the gist of the present invention.

Claims

1. An intelligent thermostat durability test system, characterized in that Including: A state modeling unit (1) for defining a state set and an extended variable set of a target thermostat, defining state transition conditions of the target thermostat according to the extended variable set and the state set, and constructing a state transition graph according to the extended variable set, the state set, and the state transition conditions; A state matching unit (2) for generating a sequence of random extended variable sets, and matching the sequence of random extended variable sets with the state transition graph according to a depth-first search algorithm to obtain state transition paths corresponding to each random extended variable set in the sequence of random extended variable sets; A test case generation unit (3) for generating test cases according to the random extended variable set and the state transition paths, converting the test cases into script instructions, and controlling the target thermostat according to the sequence of script instructions; A test determination unit (4), where the exception determination unit (4) is for monitoring the temperature of a temperature control environment controlled by the target thermostat to obtain the real-time temperature of the temperature control environment, and determining a test label of the target thermostat according to the test case and the real-time temperature, where the test label includes an exception label and a normal label.

2. The intelligent thermostat durability test system according to claim 1, characterized in that The system further includes: An exception determination unit (5) for counting the test labels of the target thermostat within a preset test period, and determining that the target thermostat is abnormal if the number of test labels that are exception labels is greater than a preset threshold; A durability prediction unit (6) for, if the number of test labels that are exception labels is not greater than a preset threshold, counting a reaction time sequence of the target thermostat within a preset test period, and predicting the durability of the target thermostat according to the reaction time sequence.

3. An intelligent thermostat durability test system according to claim 2, wherein The state set includes a standby state, a heating state, a cooling state, an over-temperature warning state, a low-temperature warning state, and a fault state; the extended variable set includes a current temperature, a target temperature, a maximum safe temperature, a minimum safe temperature, an elapsed time, a temperature change rate, and a maximum safe available time; The test case includes a state transition path of the target thermostat and a random extended variable set before each state transition of the target thermostat.

4. An intelligent thermostat durability test system according to claim 3, characterized in that, Defining the state transition conditions of the target thermostat according to the extended variable set and the state set includes: If the current temperature reaches the target temperature and the temperature change rate becomes zero, the target thermostat enters the standby state; If the current temperature is less than the target temperature, the target thermostat enters the heating state; If the current temperature is higher than the target temperature, the target thermostat enters the cooling state; If the current temperature is higher than the maximum safe temperature, the target thermostat enters the over-temperature warning state; If the current temperature is less than the minimum safe temperature, the target thermostat enters the low-temperature warning state; If the used time is higher than the maximum safe available time, the target thermostat enters a fault state.

5. An intelligent thermostat durability test system according to claim 4, characterized in that, Construct a state transition diagram according to the extended variable set, the state set, and the state transition conditions, including: Use each state in the state set as a state node of the state transition diagram; For each state node in the state transition diagram, judge whether there is a transfer path that meets the state transition conditions according to the combination of the extended variable set and the state transition conditions. If there is a transfer path that meets the state transition conditions, add a transfer edge in the state transition diagram, and the transfer edge represents the transfer from the current state to the next state; Mark the corresponding extended variable value and state transition condition on each transfer edge in the state transition diagram.

6. An intelligent thermostat durability test system according to claim 5, characterized in that, Convert the test case into a script instruction, including: Judge the signal type required by the test case according to the random extended variable set in the test case. The signal type includes a step signal and a ramp signal. When the gap between the current temperature and the target temperature is greater than a preset first gap threshold, a step signal is used; otherwise, a ramp signal is used; Encode the signal type required by the test case into a script instruction.

7. An intelligent thermostat durability test system according to claim 6, characterized in that, Determine the test label of the target thermostat according to the test case and the real-time temperature, including: Compare the target temperature required in the test case with the real-time temperature within a preset control period; If the gap between the target temperature required in the test case and the real-time temperature is greater than a preset second gap threshold, determine that the test label of the target thermostat is an abnormal label; otherwise, determine that the test label of the target thermostat is a normal label.

8. An intelligent thermostat durability test system according to claim 7, characterized in that, Statistically analyze the response time series of the target thermostat within a preset test period, including: Statistically analyze the response time of the target thermostat from the current temperature to the target temperature in the test case within a preset control period; Repeat the processing of all test cases within a preset test period to obtain the response time series of the target thermostat.

9. An intelligent thermostat durability test system according to claim 8, characterized in that, Conduct durability prediction on the target thermostat according to the response time series, including: Define the response time degradation characteristic quantity of the target thermostat according to the response time series; Define the relative increment of the response time degradation characteristic quantity; Calculate the drift parameter and diffusion coefficient of the relative increment of the response time degradation characteristic quantity based on maximum likelihood estimation; Construct the remaining durability probability density function of the target thermostat, substitute the drift parameter and diffusion coefficient of the relative increment of the response time degradation characteristic quantity into the remaining durability probability density function, and use the time corresponding to the maximum value of the remaining durability probability density function as the remaining service durability of the target thermostat.

10. An intelligent thermostat durability test system according to claim 9, characterized in that, The expression of the response time degradation characteristic quantity is as follows: X(t) = a + μt + σ B B(t); Among them, X(t) represents the reaction time degradation characteristic quantity, a represents the initial value of the reaction time, μ represents the drift coefficient, and σ B represents the diffusion coefficient, B(t) represents the standard Wiener process, and t represents the reaction time series; The expression of the relative increment is as follows: ΔX(t i ) = a + μt i + σ B ΔB(t i ); where, ΔX(t i ) represents the relative increment of the reaction time degradation characteristic quantity, t i represents the i-th reaction time in the reaction time series, and ΔB(t i ) represents the relative increment of the standard Wiener process; The expression of the remaining durability probability density function is as follows: where f(t|μ,σ B ,h) represents the remaining durability probability density function, and h represents the failure threshold.

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