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The growing numbers, complexity, and data return of space missions are driving a need for automated fault detection and diagnosis. Traditional fault monitoring techniques possess limited ability to diagnose anomalies, requiring operators to investigate extensive amounts of telemetry to isolate root causes. An important hurdle to automating fault diagnosis for complex subsystems such as attitude determination and control is that interpreting anomalies to determine a root cause is highly contextual and inherently uncertain. This paper presents a method of using one-class support vector machines to train a long short-term memory neural network to diagnose time-varying faults in a simulated CubeSat attitude determination and control subsystem. Using elements of transfer learning, the one-class support vector machines are retrained on nominal flight telemetry, allowing the neural network to diagnose faults on the flight unit. The diagnoses are robust to missing data, false positives, modeling errors, contradicting information, and multiple faults. An application is demonstrated for the LightSail 2 solar sail satellite, where the system succeeds in diagnosing both known and previously unknown attitude determination and control faults.