Fault Diagnosis of Antenna Pointing Systems using Hybrid Neural Network and Signal Processing Models
Authors: Jeff Mellstrom Padhraic Smyth
Published in: Advances in Neural Information Processing Systems 4  (NIPS 1991) (Main Conference Track), 1991

Abstract: 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 
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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 
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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). 
2.2 
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: