**1. Introduction**

Microwave vacuum tubes used in high-power radar and communications systems have a lifetime of a few thousand active hours before refurbishment is required. When one of these microwave vacuum tubes fails, it is generally impossible to determine the sequence of events leading to its failure. Each year, nearly \$100 M is spent replacing high-power microwave tubes in the fleet. In many cases (estimated at over 25%), tubes that are operating perfectly are inadvertently replaced because there is insufficient in-situ monitoring equipment available to diagnose specific problems within the system. This results in high maintenance and refurbishing costs.

At present, microprocessor-based systems with sensors are designed to monitor tube performance, provide tube protection, and record a comprehensive tube failure history. A major limitation of these systems results from the small amount of time available during the inter-pulse period of the tube for data buffering and fault analysis. The present monitoring systems work well if the microwave tube is operated with 200 or less pulses per second (pps). Normally, the radar tubes are operated at up to 1000 pps with pulse duration of a microsecond. Increasing the A/D conversion speed will, in some cases, make the situation worse, since it increases the amount of data that must be transferred and analyzed during the small time interval available. These high vacuum devices (10<sup>−</sup><sup>7</sup> –10<sup>−</sup><sup>8</sup> Torr) have electrode voltages that can run up to more than 10 kV, while their heat dissipation ranges from 100 W to 10 kW. The complexity of these systems makes them very expensive to produce, maintain, and replace. This provides a motivation for the development of alternative, more effective monitoring and diagnostic techniques.

In recent years, research has established acoustic emission (AE) sensing as a very effective technique for machine condition monitoring and analysis. This technique has been tested and evaluated in a variety of systems as an alternative to conventional techniques. A novel application of this technique is the in-situ performance monitoring of high-power microwave (HPM) tubes. This report addresses two questions: (1) Would the microwave radar tubes operating under normal or abnormal conditions be able to generate AE signals? (2) If so, can the observed signals provide signatures to discriminate among different types of failures?

Acoustic emission (AE) may be defined as stress or pressure waves generated during dynamic processes in materials. AE is elastic energy that is spontaneously released by materials when they undergo deformation and is typically generated in the form of ultrasound waves created by local mechanical instabilities within the material. AE is generally detected by means of ultrasonic transducers coupled to the material with a suitable coupler to decrease impedance mismatch. Among many mechanisms that produce AE activity, the principal mechanisms are crack initiation and growth, magneto-mechanical realignment or growth of magnetic domains (Barkhausen effect), dislocation movements, twining, phase changes, fracture of brittle inclusions, fiber breakage in composite materials, chemical activity, and cavitation. Some stimuli are necessary to trigger acoustic emissions. Stress, a major type of stimuli, may be mechanically applied, thermally generated, or caused by a changing magnetic field. Acoustic emission could thus act as a passive nondestructive technique (NDT) and be used to monitor and analyze normal and abnormal performance of microwave vacuum tubes.

The research presented in this paper demonstrates the detection of anomalous RF pulses and system failures using acoustic emission and magneto resistive or inductively coupled current sensors. It also demonstrates the ability to discriminate among the different types of failures. This innovative system has been tested on a klystron as part of an AN/SPS-49(V)5 radar system and on a radar system magnetron (Model 2J55). An added feature of this innovative system is the fact that the outputs from the sensors have been successfully interfaced with the ICAS (Integrated Condition Assessment System) software currently used by the U.S. Navy.

Once the output of the sensors was integrated with the ICAS software, a method was developed for integrating a plurality of sensor data in such a way to produce greater information than any individual sensor or combination of sensors. This method is particularly useful for detecting and predicting failures and for life cycle monitoring in microwave vacuum devices. This method has been defined as a virtual sensor.
