Padhraic Smyth, Jeff Mellstrom
We describe in this paper a novel application of neural networks to system health monitoring of a large antenna for deep space communications. The paper outlines our approach to building a monitoring system using hybrid signal processing and neural network techniques, including autoregressive modelling, pattern recognition, and Hidden Markov models. We discuss several problems which are somewhat generic in applications of this kind - in particular we address the problem of detecting classes which were not present in the training data. Experimental results indicate that the proposed system is sufficiently reliable for practical implementation.
1 Background: The Deep Space Network
The Deep Space Network (DSN) (designed and operated by the Jet Propulsion Lab(cid:173) oratory (JPL) for the National Aeronautics and Space Administration (NASA)) is unique in terms of providing end-to-end telecommunication capabilities between earth and various interplanetary spacecraft throughout the solar system. The ground component of the DSN consists of three ground station complexes located in California, Spain and Australia, giving full 24-hour coverage for deep space com(cid:173) munications. Since spacecraft are always severely limited in terms of available transmitter power (for example, each of the Voyager spacecraft only use 20 watts to transmit signals back to earth), all subsystems of the end-to-end communica(cid:173) tions link (radio telemetry, coding, receivers, amplifiers) tend to be pushed to the 667
Smyth and Mellstrom
absolute limits of performance. The large steerable ground antennas (70m and 34m dishes) represent critical potential single points of failure in the network. In partic(cid:173) ular there is only a single 70m antenna at each complex because of the large cost and calibration effort involved in constructing and operating a steerable antenna of that size - the entire structure (including pedestal support) weighs over 8,000 tons.
The antenna pointing systems consist of azimuth and elevation axes drives which respond to computer-generated trajectory commands to steer the antenna in real(cid:173) time. Pointing accuracy requirements for the antenna are such that there is little tolerance for component degradation. Achieving the necessary degree of positional accuracy is rendered difficult by various non-linearities in the gear and motor ele(cid:173) ments and environmental disturbances such as gusts of wind affecting the antenna dish structure. Off-beam pointing can result in rapid fall-off in signal-to-noise ratios and consequent potential loss of irrecoverable scientific data from the spacecraft.
The pointing systems are a complex mix of electro-mechanical and hydraulic com(cid:173) ponents. A faulty component will manifest itself indirectly via a change in the char(cid:173) acteristics of observed sensor readings in the pointing control loop. Because of the non-linearity and feedback present, direct causal relationships between fault condi(cid:173) this makes manual fault tions and observed symptoms can be difficult to establish - diagnosis a slow and expensive process. In addition, if a pointing problem occurs while a spacecraft is being tracked, the antenna is often shut-down to prevent any potential damage to the structure, and the track is transferred to another antenna if possible. Hence, at present, diagnosis often occurs after the fact, where the orig(cid:173) inal fault conditions may be difficult to replicate. An obvious strategy is to design an on-line automated monitoring system. Conventional control-theoretic models for fault detection are impractical due to the difficulties in constructing accurate an alternative is to learn the symptom-fault models for such a non-linear system - mapping directly from training data, the approach we follow here.
2 Fault Classification over Time
2.1 Data Collection and Feature Extraction
The observable data consists of various sensor readings (in the form of sampled time series) which can be monitored while the antenna is in tracking mode. The approach we take is to estimate the state of the system at discrete intervals in time. A feature vector ~ of dimension k is estimated from sets of successive windows of sensor data. A pattern recognition component then models the instantaneous estimate of the posterior class probability given the features, p(wd~), 1 :::; i :::; m. Finally, a hidden Markov model is used to take advantage of temporal context and estimate class probabilities conditioned on recent past history. This hierarchical pattern of information flow, where the time series data is transformed and mapped into a categorical representation (the fault classes) and integrated over time to enable robust decision-making, is quite generic to systems which must passively sense and monitor their environment in real-time.
Experimental data was gathered from a new antenna at a research ground-station at the Goldstone DSN complex in California. We introduced hardware faults in a
Fault Diagnosis of Antenna Pointing Systems
controlled manner by switching faulty components in and out of the control loop. Obtaining data in this manner is an expensive and time-consuming procedure since the antenna is not currently instrumented for sensor data acquisition and is located in a remote location of the Mojave Desert in Southern California. Sensor variables monitored included wind speed, motor currents, tachometer voltages, estimated antenna position, and so forth, under three separate fault conditions (plus normal conditions) .
The time series data was segmented into windows of 4 seconds duration (200 sam(cid:173) pies) to allow reasonably accurate estimates of the various features. The features consisted of order statistics (such as the range) and moments (such as the vari(cid:173) ance) of particular sensor channels. In addition we also applied an autoregressive(cid:173) exogenous (ARX) modelling technique to the motor current data, where the ARX coefficients are estimated on each individual 4-second window of data. The autore(cid:173) gressive representation is particularly useful for discriminative purposes (Eggers and Khuon, 1990).
State Estimation with a Hidden Markov Model
If one applies a simple feed-forward network model to estimate the class probabilities at each discrete time instant t, the fact that faults are typically correlated over time is ignored. Rather than modelling the temporal dependence of features, p(.f.(t)I.f.(t- 1), ... ,.f.(0)), a simpler approach is to model temporal dependence via the class variable using a Hidden Markov Model (HMM). The m classes comprise the Markov model states. Components of the Markov transition matrix A (of dimension m x m) are specified subjectively rather than estimated from the data, since there is no reliable database of fault-transition information available at the component level from which to estimate these numbers. The hidden component of the HMM model arises from the fact that one cannot observe the states directly, but only indirectly via a stochastic mapping from states to symptoms (the features). For the results reported in this paper, the state probability estimates at time t are calculated using all the information available up to that point in time. The probability state vector is denoted by p(s(t)). The probability estimate of state i at time t can be calculated recursively via the standard HMM equations: