**7. Virtual sensor**

*Acoustics of Materials*

**6. Integration with ICAS**

both crisp logic and fuzzy logic rules.

The next step was to integrate the laboratory nondestructive AE test system into field applications for performance monitoring of a high-power microwave radar tube [8]. Currently, the Navy is using ICAS version 4.11 (IDAX Inc. Norfolk,

The integration with ICAS required substantial electronics development in order to process, into a form compatible with ICAS, the acoustic emission and current signals collected during the normal and abnormal functioning of highpower radar tubes. An OPTO22 SNAP B3000 BRAIN unit was utilized to collect the data from the current and acoustic sensors. Two interface circuits were developed, one for each type of sensor, to interface with the ICAS software. **Figures 12** and **13** show the block diagrams for the developed electronics. The details of the electronics are beyond the scope of this paper. Further details on the ICAS interface and the electronics designed for this effort can be found in

VA) for the performance monitoring of mechanical systems on ships. The Integrated Condition Assessment System (ICAS) is a 32-bit Microsoft Windows NT based plant data analysis and integration tool. It is a predictive maintenance program that combines state-of-the-art performance monitoring techniques with computerized maintenance management. ICAS provides data acquisition and display, equipment analysis, diagnostic recommendations, and decision support information to plant operators and maintenance personnel. The system also provides user-defined performance alarms that alert the operator to machine problems. It provides the hybrid diagnostic system (HDS) diagnostic advisories that assist in diagnosing approaching failures and initiating the restoring process. The hybrid intelligent system is a fault-modeling environment that comprises

**52**

**Figure 12.**

Ref. [8].

*Interface circuit for acoustic emission sensor.*

One of the main advantages of the ICAS software is that it allows mathematical manipulation and combination of inputs from real sensors. This is essential in the development of what has been defined in this report as a virtual sensor. The generalized concept behind the virtual sensor is that a characteristic signature of a failure, denoted by F, is a function of more than one parameter (x1, x2, x3,…,xi). Thus, the characteristic signature of the failure, F, can be written mathematically as

$$\mathbf{F} = \mathbf{F} \{ \mathbf{x}\_1, \mathbf{x}\_2, \mathbf{x}\_3, \dots, \mathbf{x}\_l \} \tag{3}$$

$$\mathbf{F} = \mathbf{F} \{ \mathbf{x}\_1, \mathbf{x}\_2, \mathbf{x}\_3, \dots, \mathbf{x}\_l \} = \sum\_{l=1}^{\overline{n}} \sum\_{l=1}^{\overline{n}} \sum\_{k=0}^{\otimes \overline{n}} \sum\_{l=0}^{\otimes \overline{n}} C\_{ijkl} \mathbf{X}\_i^k \mathbf{X}\_j^l \tag{4}$$

where Cijkl are calculation coefficients and Xk and Xl correspond to each of n number of sensor parameters. The function F can generally be represented by a polynomial expansion.

As an example, if the failure is a function of just two parameters, then

$$\mathbf{F} = \mathbf{F}(\mathbf{x}\_1, \mathbf{x}\_2) = \mathbf{a} + \mathbf{b}\mathbf{x}\_1 + \mathbf{c}\mathbf{x}\_2 + \mathbf{d}\mathbf{x}\_1\mathbf{x}\_2 + \mathbf{e}\mathbf{x}\_1^2\mathbf{x}\_2 + \mathbf{f}\mathbf{x}\_1\mathbf{x}\_2 + \dots \tag{4}$$

where the coefficients Cijkl are represented by a, b, c, d, …, and the functional parameters X<sup>k</sup> are represented by x1 and x2. Note that this expansion does not specifically require that the parameters have the same units or appear correlated at first appearance. Monitoring the value of F will therefore provide a measure of the system status and/or identify or differentiate failures. Using the virtual sensor formalism allows one to concatenate sensor information to provide more information than normally derived from either sensor alone or used in normal combination. An alternative approach to simply combining sensor data in the manner just described

#### *Acoustics of Materials*

would be to define specific ranges of values for each sensor parameter and assign weighted values to each specified range.

Single sensor data can be used to detect faulty behavior but cannot readily differentiate or identify failures in the trigger sources, microwave tube, or other modulator electronics. This virtual sensor method can be applied to the monitoring of anomalous acoustic and cathode current pulses which are characteristic of a failed RF pulse. More specifically, this method can be used to count the number of anomalous pulses from each one of the sensor interfaces described in Section 4 and **Figures 12** and **13**.

To illustrate the advantages of a virtual sensor, consider the combined failure function, F, for a particular placement of an acoustic emission sensor whose parameter is represented by EAE and a current sensor whose parameter is represented by Ic, where

$$\mathbf{F} = |I\_C| + \int\_0^t E\_{AE}(t) \, dt. \tag{5}$$

A virtual sensor for this function which represents the magnitude of the current pulse and the integrated AE energy can be used to add the number of faulty counts from the two real sensors producing a virtual sensor output. Long-term trends and analysis can be used to characterize the behavior and identify trend signatures for different types of microwave tubes. This synergistic effect of virtual sensing adds diagnostic and, more importantly, prognostic capabilities to the ICAS or any other monitoring system. The failure function, F, can be adapted to the needs and complexity of any system and can be defined to extract specific information required from that system.

This technique was demonstrated on a magnetron tube (2J55). The experiment was conducted at the Microwave Tubes Built-In Test Project laboratory at SPAWAR Systems Center, San Diego. The upper section of **Figure 14** shows an ICAS screen capture of the cathode current (green) and acoustic emission (yellow) faulty pulse counts. The lower section shows the virtual sensor outputs where two failure functions, F1 and F2, have been defined and measured. Function F1 = x−y (yellow), where the difference between the outputs of the two real sensors represents virtual sensor 1, with x = cathode current and y = acoustic emission; and function F2 = x\*y (green), where the product of the outputs of the

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*Automated Classification of Microwave Transmitter Failures Using Virtual Sensors*

two real sensors represents virtual sensor 2. These figures represent a 2-minute span of data collected during a period where the microwave tube was being

The experimental results presented in this paper demonstrate the use of advanced acoustic emission techniques as a nondestructive testing method for insitu performance monitoring of high-power radar tubes such as pulsed magnetrons, TWTs, and klystrons. It was shown experimentally that changes in the amplitude and frequency content of the cathode current pulses are strongly correlated to changes in acoustic emission pulse energy both under normal and stressed operational conditions. The necessary electronics were developed to successfully interface the outputs of the current and acoustic emission sensors with the integrated condition assessment system (ICAS) used by the U.S. Navy. ICAS was used to demonstrate the use of virtual sensors where data from real sensors are combined into a failure functions F and captured in trend and single sensor format. In summary, this technique has demonstrated the unique ability to monitor, detect, and identify microwave tube performance. It has also demonstrated the ability to be utilized as a diagnostic tool by looking at long-term performance trends. More experimental research is needed to identify particular trends and signatures for the behavior of different types of microwave tubes under different circumstances to provide a fully

This work was supported by the Office of Naval Research (ONR) under the auspices of Dr. Phillip Abraham (ONR-331) and Dr. Ignacio Perez (ONR-332); and in part by ONR's American Society for Engineering Education (ASEE) Summer

The ICAS trending features were also demonstrated in this experiment. **Figure 15** shows a trend of the current faults count (vertical axis) versus the acoustic faults count (horizontal axis). This trend forms a band with few scattered points, indicating a strong correlation between both types of faulty counts and confirming the results obtained in

*DOI: http://dx.doi.org/10.5772/intechopen.81652*

stressed to produce faulty RF pulses.

*Graph of faulty cathode current counts versus faulty acoustic emission counts.*

prognostic capability for microwave tube systems.

Faculty Program, managed by Mr. Timothy Manicom.

previous experiments.

**Acknowledgements**

**8. Conclusion**

**Figure 15.**

**Figure 14.** *Cathode current and acoustic emission faulty pulses and failure functions.*

*Automated Classification of Microwave Transmitter Failures Using Virtual Sensors DOI: http://dx.doi.org/10.5772/intechopen.81652*

**Figure 15.** *Graph of faulty cathode current counts versus faulty acoustic emission counts.*

two real sensors represents virtual sensor 2. These figures represent a 2-minute span of data collected during a period where the microwave tube was being stressed to produce faulty RF pulses.

The ICAS trending features were also demonstrated in this experiment. **Figure 15** shows a trend of the current faults count (vertical axis) versus the acoustic faults count (horizontal axis). This trend forms a band with few scattered points, indicating a strong correlation between both types of faulty counts and confirming the results obtained in previous experiments.
