Anomaly Detection for Large Time Series Data
Following J.Tukey, you should plot, draw graphs, visualize, etc... until you have a solid pack of examples.

Then make Tukey' fences on each of the 30 parameters. Let $q_1$ and $q_3$ be the 1st and 3rd quartiles, $d=q_3-q_1$ the inter-quartile distance, and define as outlier any observation outside the interval $q_1-k\cdot d &lt; x &lt; q_3+k\cdot d$, where $k$ is a constant. Traditionally, $k=1.5$ indicates an outlier and $k=3$ indicates the data is far out. However, the real value of $k$ should be tested against your examples.

Then make a cluster analysis (with a k-nearest neighbor) and define as outlier any point isolated in one cluster. Again, use your example to test various values of $k$.