A smart fault diagnosis device and method with BIT capability

Abstract

This invention discloses an intelligent fault diagnosis device and method with BIT (Block Interruption Testing) capability. The method includes: connecting one or more devices under test (DUTs) to the intelligent fault diagnosis device, wherein the DUTs are field replaceable units (LRUs); installing multiple sensors inside and at the external interfaces of each LRU, and transmitting the collected data to a central processing unit; analyzing the collected data using an adaptive algorithm to output a fault risk score A for the current system state; adjusting the parameters of a fault prediction model λ(t) based on the fault risk score A; and obtaining an overall fault diagnosis index F based on the fault risk score A and the fault prediction model λ(t). D This invention generates fault diagnosis reports. It improves fault diagnosis accuracy, enhances fault prediction capabilities, adapts to complex operating conditions, reduces maintenance costs and downtime, provides real-time monitoring and feedback mechanisms, offers scalability and compatibility, and supports data-driven intelligent decision support.

Description

Technical Field

[0001] This invention belongs to the field of computer systems engineering, and particularly relates to an intelligent fault diagnosis device and method with BIT capability. Background Technology

[0002] In modern aerospace, military, and high-tech fields, equipment monitoring and fault diagnosis based on Built-in Test (BIT) technology are widely used. BIT systems, by integrating sensors and self-testing programs within the equipment, can monitor the equipment's operating status in real time, identify potential faults, and issue alerts before faults occur. The goal of this technology is to ensure equipment reliability and safety, thereby reducing maintenance costs and downtime. However, current BIT systems still face several technical challenges in practical applications, limiting their effectiveness and accuracy in fault diagnosis.

[0003] First, traditional BIT (Best In-Time) technology often relies on static thresholds to determine the health status of equipment. While this method may be effective in simple cases, static thresholds are prone to false alarms or missed alarms in complex and dynamically changing systems. For example, when equipment operates under high load or extreme environments, its monitored parameters (such as temperature, current, and voltage) may fluctuate naturally, rather than due to a fault. In such cases, judgments based on static thresholds may be misdiagnosed as faults, leading to unnecessary maintenance and repairs.

[0004] Secondly, BIT systems also have limitations in data processing and analysis capabilities. With the increasing number of sensors and the accumulation of monitoring data, effectively processing and analyzing this data becomes a critical issue. Traditional fault diagnosis methods often fail to fully utilize real-time data for dynamic analysis, leading to inaccurate diagnostic results. Furthermore, the lack of flexible data processing algorithms makes the system insufficiently responsive to sudden faults and unable to adjust monitoring strategies in a timely manner.

[0005] Furthermore, current BIT systems often lack intelligent fault prediction capabilities. Although some systems possess fault detection functions, their ability to predict potential faults remains insufficient. The probability of fault occurrence and influencing factors are typically complex and variable, making it difficult to adapt to changes in equipment operating conditions by relying solely on historical fault records. Therefore, how to accurately estimate the fault occurrence rate and dynamically adjust it based on the current equipment status is a problem that BIT systems urgently need to solve.

[0006] Furthermore, the fault diagnosis capabilities of existing BIT systems are often limited to monitoring a single parameter, lacking systematic and comprehensive approaches. For complex systems, anomalies in a single parameter cannot fully reflect the health status of the equipment. Therefore, how to comprehensively assess the overall health status of the equipment through multi-parameter fusion analysis becomes an important direction for improving the accuracy of fault diagnosis. Summary of the Invention

[0007] To address the shortcomings of the existing technology, this invention provides an intelligent fault diagnosis method with BIT capability, comprising the following steps:

[0008] Step S101: Connect one or more devices under test to the intelligent fault diagnosis device, wherein the one or more devices under test are field replaceable units (LRUs).

[0009] Step S103: Install multiple sensors inside and at the external interface of each LRU, and transmit the collected data to the central processing unit of the intelligent fault diagnosis device.

[0010] Step S105: The central processing unit uses an adaptive algorithm to analyze the collected data and outputs a fault risk score A for the current system status.

[0011] Step S107: Based on the fault risk score A, adjust the parameters of the fault prediction model λ(t);

[0012] Step S109: Based on the fault risk score A and the fault prediction model λ(t), obtain the overall fault diagnosis index F. D Generate a fault diagnosis report.

[0013] In step S101, the test circuit is directly embedded in the LRU.

[0014] The sensors include a current sensor, a voltage sensor, and a temperature sensor.

[0015] In step S105, the fault risk score A of the current system state is calculated using the following formula:

[0016] Where M is the sample size, w j f is the weight of the j-th sample. j (X) is the prediction function based on the j-th sample of input X.

[0017] In step S107, the failure occurrence rate λ(t) of the failure prediction model is obtained using the following formula:

[0018] λ(t)=(α+k·A)·e -(β-m·A)t+γ, where α represents the initial failure rate; k represents the adjustment coefficient, used to control the influence of the adaptive algorithm output A on the initial failure rate α; β is the decay factor, representing the rate at which the failure rate decreases over time; m is the adjustment coefficient, used to control the influence of the adaptive algorithm output A on the decay factor β; and γ is the minimum failure rate, indicating that under any circumstances, the probability of system failure will not be lower than this value.

[0019] In step S109, the overall fault diagnosis index F is obtained using the following formula. D ,

[0020] Among them, S i (t) represents the sensor data of the i-th circuit at time t; D(t) represents the total monitoring data at time t; N represents the number of devices under test; T represents the total monitoring time period, and λ(t) represents the duration of data acquisition; λ(t) represents the failure rate; w i The importance weight of the i-th device is represented by γ(D(t)); γ(D(t)) represents the normalization function used to handle the influence of the total monitoring data D(t); F D ∈[0,1], where 0 indicates the possibility of no failure, 1 indicates the certainty of failure, and the system is in a state of complete failure.

[0021] The fault diagnosis report in step S109 includes the fault type, fault location, and recommended handling measures to facilitate subsequent maintenance.

[0022] The fault diagnosis report can display fault information in real time and provide query and recording functions through the display interface of the intelligent fault diagnosis device to facilitate quick response by operators.

[0023] The intelligent fault diagnosis device isolates the detected fault information to the field replaceable unit (LRU) for subsequent maintenance and replacement.

[0024] This invention also proposes an intelligent fault diagnosis device with BIT capability, including...

[0025] A functional plug-in box is used to connect one or more devices under test to an intelligent fault diagnosis device, wherein the one or more devices under test are field replaceable units (LRUs).

[0026] Sensors are installed inside each LRU and at external interfaces to transmit the collected data to the central processing unit of the intelligent fault diagnosis device.

[0027] The central processing unit is used to analyze the collected data using an adaptive algorithm and output a fault risk score A for the current system status.

[0028] The central processing unit is also used to adjust the parameters of the fault prediction model λ(t) based on the fault risk score A; and to obtain the overall fault diagnosis index FD based on the fault risk score A and the fault prediction model λ(t), and generate a fault diagnosis report.

[0029] A display screen, which provides a display interface for querying and displaying information;

[0030] A UPS unit is used to provide uninterrupted power.

[0031] Compared with the prior art, the present invention has the following advantages:

[0032] By introducing an adaptive algorithm, this invention can analyze and adjust the fault risk score A in real time, reflecting the real-time health status of the equipment. Compared with the traditional static threshold method, this dynamic adjustment mechanism significantly improves the accuracy of fault diagnosis and reduces the occurrence of false alarms and missed alarms.

[0033] By using the output of the adaptive algorithm as input to the fault prediction model, parameters of the fault occurrence rate (such as the initial fault occurrence rate α and the decay factor β) can be dynamically adjusted. This method makes the prediction of fault occurrence probability more accurate, enabling early identification of potential faults and thus providing a reliable basis for maintenance decisions.

[0034] Traditional fault diagnosis methods often struggle to adapt to changes in equipment operating under complex conditions. This invention, by real-time monitoring of multiple operating parameters (such as temperature, voltage, and current) and combining them with adaptive algorithms, can comprehensively assess the overall health status of the equipment. This multi-parameter fusion analysis method effectively enhances the system's adaptability, ensuring fault identification and prediction under different operating conditions.

[0035] By accurately predicting faults, businesses can implement preventative maintenance, avoiding unnecessary repairs and downtime. This not only reduces maintenance costs but also improves equipment operating efficiency, extends equipment lifespan, and ensures the continuity and stability of production activities.

[0036] The system can dynamically adjust its fault diagnosis strategy based on the equipment's operating status and environmental changes. This flexibility enables the system to respond quickly to sudden failures, improving the overall system's robustness and reliability.

[0037] It boasts excellent scalability, adapting to different types and sizes of equipment. Whether in aerospace, military, or other high-tech fields, it can be customized and applied to meet specific needs, enhancing system compatibility and flexibility.

[0038] By deeply integrating and analyzing historical and real-time data, this invention provides data-driven intelligent decision support for equipment managers. This data-based decision-making mechanism improves the scientific validity and effectiveness of maintenance strategies, helping enterprises optimize resource allocation. Attached Figure Description

[0039] The above and other objects, features, and advantages of exemplary embodiments of the present disclosure will become readily apparent upon reading the following detailed description with reference to the accompanying drawings. In the drawings, several embodiments of the present disclosure are illustrated by way of example and not limitation, and like or corresponding reference numerals denote like or corresponding parts, wherein:

[0040] Figure 1 This is a flowchart illustrating an intelligent fault diagnosis method with BIT capability according to an embodiment of the present invention. Detailed Implementation

[0041] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0042] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0043] It should be understood that although the terms first, second, third, etc., may be used to describe... in the embodiments of the present invention, these... should not be limited to these terms. These terms are only used to distinguish... For example, first... may also be referred to as second... without departing from the scope of the embodiments of the present invention, and similarly, second... may also be referred to as first...

[0044] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0045] Depending on the context, the words “if” or “suppose” as used here can be interpreted as “when” or “in response to determination” or “in response to detection.” Similarly, depending on the context, the phrases “if determination” or “if detection (of the stated condition or event)” can be interpreted as “when determination” or “in response to determination” or “when detection (of the stated condition or event)” or “in response to detection (of the stated condition or event).”

[0046] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that an article or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the article or device that includes said element.

[0047] The optional embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0048] BIT (Built-In Test) refers to integrating testing functions into the device, enabling the device to perform self-testing and fault diagnosis autonomously. BIT observability means that when a fault occurs, the system can effectively monitor, identify, and report fault information, ensuring that the fault can be located to a specific functional unit (LRU, Line Replaceable Unit).

[0049] Example 1

[0050] like Figure 1 As shown, this invention discloses an intelligent fault diagnosis method with BIT capability, comprising the following steps:

[0051] Step S101: Connect one or more devices under test to the intelligent fault diagnosis device, wherein the one or more devices under test are field replaceable units (LRUs).

[0052] Step S103: Install multiple sensors inside and at the external interface of each LRU, and transmit the collected data to the central processing unit of the intelligent fault diagnosis device.

[0053] Step S105: The central processing unit uses an adaptive algorithm to analyze the collected data and outputs a fault risk score A for the current system status.

[0054] Step S107: Based on the fault risk score A, adjust the parameters of the fault prediction model λ(t);

[0055] Step S109: Based on the fault risk score A and the fault prediction model λ(t), obtain the overall fault diagnosis index F. D Generate a fault diagnosis report.

[0056] Example 2

[0057] The present invention proposes an intelligent fault diagnosis method with BIT capability, comprising the following steps:

[0058] Step S101: Connect one or more devices under test to the intelligent fault diagnosis device, wherein the one or more devices under test are field replaceable units (LRUs).

[0059] Step S103: Install multiple sensors inside and at the external interface of each LRU, and transmit the collected data to the central processing unit of the intelligent fault diagnosis device.

[0060] Step S105: The central processing unit uses an adaptive algorithm to analyze the collected data and outputs a fault risk score A for the current system status.

[0061] Step S107: Based on the fault risk score A, adjust the parameters of the fault prediction model λ(t);

[0062] Step S109: Based on the fault risk score A and the fault prediction model λ(t), obtain the overall fault diagnosis index F. D Generate a fault diagnosis report.

[0063] In step S101, the test circuit is directly embedded in the LRU.

[0064] The sensors include a current sensor, a voltage sensor, and a temperature sensor.

[0065] In step S105, the fault risk score A of the current system state is calculated using the following formula:

[0066] Where M is the sample size, w j f is the weight of the j-th sample. j (X) is the prediction function based on the j-th sample of input X.

[0067] The value of A represents the current level of system failure risk. The higher the score, the greater the likelihood of failure. Based on this score, the system can determine whether immediate maintenance or replacement measures are necessary.

[0068] function f j (X) is a feature function based on the input X. Taking three sensors (temperature T, voltage V, and current I) as an example, the feature function f is defined.j (X).

[0069] Suppose we define a characteristic function for each sensor, using a combination of linear and nonlinear terms to reflect the sensor's influence. This can be expressed as:

[0070]

[0071] Then the final expression for f(x) is:

[0072] The parameters (a1, a2, a3, b1, b2, b3) of these functions can be trained and optimized based on historical data to improve the accuracy of the model.

[0073] In step S107, the failure occurrence rate λ(t) of the failure prediction model is obtained using the following formula, representing the failure probability at time t. It reflects the likelihood of a system failure at a specific point in time.

[0074] λ(t)=(α+k·A)·e -(β-m·A)t +γ, where α represents the initial failure rate, which is the basic failure rate of the system when there are no other influencing factors. It is usually obtained through historical data analysis, specifically by statistically analyzing past failure records and calculating the number of failures per unit time; k represents the adjustment coefficient, which controls the influence of the adaptive algorithm output A on the initial failure rate α. It can be determined through experiments or optimization algorithms, and is usually adjusted based on historical failure data to ensure the accuracy of the model under different conditions; β is the decay factor, which represents the rate at which the failure rate decreases over time. A higher β value means that the failure rate decreases rapidly. It can be obtained by fitting historical failure data, usually using regression analysis or maximum likelihood estimation methods; m is the adjustment coefficient, which controls the influence of the adaptive algorithm output A on the decay factor β. Similar to k, it can be determined by analyzing historical data and experimental results to ensure that the system response is reasonable under different conditions; γ is the minimum failure rate, which indicates that the probability of system failure will not be lower than this value under any circumstances. It can be regarded as the baseline failure rate of the system. It is usually calculated by analyzing historical data to determine the failure rate under optimal conditions, or determined by theoretical derivation.

[0075] For example, historical failure data of the system is collected, including the time, type, and frequency of failures. Statistical analysis is used to calculate the average number of failures per unit time, yielding α. Experiments are conducted to observe the impact of different values ​​of A on the failure rate, using optimization algorithms (such as grid search or genetic algorithms) to determine the optimal values ​​of adjustment coefficients k and m. Regression analysis of historical data is used to fit the value of β, typically using statistical software or a custom-written program. The lowest probability of failure under optimal conditions is calculated, yielding γ.

[0076] In step S109, the overall fault diagnosis index F is obtained using the following formula. D ,

[0077] Among them, S i (t) represents the sensor data of the i-th circuit at time t; D(t) represents the total monitoring data at time t; N represents the number of devices under test; T represents the total monitoring time period, and λ(t) represents the duration of data acquisition; λ(t) represents the failure rate; w i The importance weight of the i-th device is represented by γ(D(t)); γ(D(t)) represents the normalization function used to handle the influence of the total monitoring data D(t); F D ∈[0,1], where 0 indicates the possibility of no failure, 1 indicates the certainty of failure, and the system is in a state of complete failure.

[0078] The fault diagnosis report in step S109 includes the fault type, fault location, and recommended handling measures to facilitate subsequent maintenance.

[0079] The fault diagnosis report can display fault information in real time and provide query and recording functions through the display interface of the intelligent fault diagnosis device to facilitate quick response by operators.

[0080] The system should feature a user-friendly interface that clearly displays fault information, diagnostic results, and suggested corrective actions to help operators take quick action. It should also include fault information logging and query capabilities to allow maintenance personnel to analyze historical fault data and optimize subsequent testing and maintenance strategies.

[0081] The intelligent fault diagnosis device isolates the detected fault information to the field replaceable unit (LRU) for subsequent maintenance and replacement.

[0082] Example 3

[0083] This invention also proposes an intelligent fault diagnosis device with BIT capability, comprising:

[0084] A functional plug-in box is used to connect one or more devices under test to an intelligent fault diagnosis device, wherein the one or more devices under test are field replaceable units (LRUs).

[0085] Sensors are installed inside each LRU and at external interfaces to transmit the collected data to the central processing unit of the intelligent fault diagnosis device.

[0086] The central processing unit is used to analyze the collected data using an adaptive algorithm and output a fault risk score A for the current system status.

[0087] The central processing unit is also used to adjust the parameters of the fault prediction model λ(t) based on the fault risk score A; and to obtain the overall fault diagnosis index FD based on the fault risk score A and the fault prediction model λ(t), and generate a fault diagnosis report.

[0088] A display screen, which provides a display interface for querying and displaying information;

[0089] A UPS unit is used to provide uninterrupted power.

[0090] Example 4

[0091] This disclosure provides a non-volatile computer storage medium storing computer-executable instructions that can perform the steps described in the above embodiments.

[0092] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0093] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0094] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (AN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0095] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0096] The units described in the embodiments of this disclosure can be implemented in software or hardware. The names of the units are not, in some cases, intended to limit the specific unit.

[0097] The preferred embodiments of the present invention have been described above to make the spirit of the present invention clearer and easier to understand, and are not intended to limit the present invention. All modifications, substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope summarized by the appended claims.

Claims

1. A method for intelligent fault diagnosis with BIT capability, characterized in that, The method comprises the following steps: S101, connecting one or more devices to be tested to the intelligent fault diagnosis device, wherein the one or more devices to be tested are LRUs; S103, arranging a plurality of sensors inside each LRU and at external interfaces, and transmitting collected data to a central processing unit of the intelligent fault diagnosis device; S105, the central processing unit analyzes the collected data by using an adaptive algorithm, and outputs a fault risk score A of a current system state; S107, adjusting parameters of a fault prediction model λ(t) based on the fault risk score A; Step S109, based on the failure risk score A and the failure prediction model λ(t), obtaining an overall failure diagnosis index F D generating a failure diagnosis report; In the step S107, the fault occurrence rate λ(t) of the fault prediction model is obtained by using the following formula, where a represents the initial failure occurrence rate; k represents an adjustment coefficient for controlling the degree of influence of the adaptive algorithm output A on the initial failure occurrence rate ; β is a decay factor representing the rate of decline of the failure occurrence rate over time; m is an adjustment coefficient for controlling the degree of influence of the adaptive algorithm output A on the decay factor ; and γ is the minimum failure occurrence rate, representing the value below which the failure occurrence probability of the system will not fall under any circumstances. The overall fault diagnosis index F is obtained in the step S109 by using the following formula D , wherein, D(t) represents the total monitoring data at time t; N represents the number of devices under test; T represents the total time period of monitoring, represents the duration of data collection; λ(t) represents the failure occurrence rate; w i represents the importance weight of the ith device; γ(D(t)) represents a normalization function for processing the influence of the total monitoring data D(t); , 0 represents the possibility of no failure occurrence, 1 represents the certainty of failure occurrence, and the system is in a complete failure state.

2. The method of claim 1, wherein, In the step S101, the test circuit is directly embedded in the LRU.

3. The method of claim 1, wherein, The sensors comprise current sensors, voltage sensors and temperature sensors.

4. The method of claim 1, wherein, In the step S105, the fault risk score A of the current system state is calculated by using the following formula: where M is the number of samples, is the weight of the jth sample, is the prediction function based on the jth sample of input X.

5. The method of claim 1, wherein, In the step S109, the fault diagnosis report comprises a fault type, a fault location and a recommended treatment measure, so as to facilitate subsequent maintenance.

6. The method of any one of claims 1-5, wherein, The fault diagnosis report can display fault information in real time, and provides query and record functions through a display interface of a display screen of the intelligent fault diagnosis device, so as to facilitate quick response of an operator.

7. The method of any one of claims 1-5, wherein, The intelligent fault diagnosis device isolates detected fault information to the LRUs, so as to facilitate subsequent maintenance and replacement.

8. A device using the intelligent fault diagnosis method with BIT capability as claimed in claim 1, comprising: a function plug-in box for connecting one or more devices to be tested to the intelligent fault diagnosis device, wherein the one or more devices to be tested are LRUs; a sensor for being arranged inside each LRU and at external interfaces, and transmitting collected data to a central processing unit of the intelligent fault diagnosis device; a central processing unit for analyzing the collected data by using an adaptive algorithm, and outputting a fault risk score A of a current system state; the central processing unit is further configured to adjust parameters of a fault prediction model λ(t) based on the fault risk score A, and obtain an overall fault diagnosis index FD based on the fault risk score A and the fault prediction model λ(t), and generate a fault diagnosis report; a display screen for providing a query and display interface; a UPS unit for providing an uninterrupted power supply.

Images