Recent DiscussionsMost RecentNewest TopicsMost LikesSolutionsTagged:TagRe: Detecting anomalies in oil&gas machines Ricardo Gutierrez, email me adieldar@microsoft.com and we shall set it Re: Detecting anomalies in oil&gas machines Ricardo Gutierrez , you can do training/scoring on ADX using our inline python() plugin that can run Python workloads in distributed manner on secure sandbox on ADX compute nodes. The Python image is based on Anaconda, but extended with Deep Learning packages like Tensorflow, keras, Pytorch and others. You can also consume and score ONNX models. We can have a session on that Re: Detecting anomalies in oil&gas machines Ricardo Gutierrez , you can read about anomaly detection and forecasting in ADX for preventive maintenance and other scenarios here here. Specific time series functions for anomaly detection are series_decompose_anomalies() for point anomalies, and series_fit_2lines() for change point detection. These functions are optimized to process thousands of time series in seconds Re: Compare count of errors in last 12h against last multiple periods of 12h Hi Preben902 You can do the following query: let counts_tbl = materialize(Event | where TimeGenerated > ago(7d) | where EventLevelName == "Warning" or EventLevelName == "Error" | summarize count() by bin(TimeGenerated, 12h) | order by TimeGenerated desc | extend rid = row_number() ); counts_tbl | where rid > 1 // all but current 12h bin | summarize maxi = max(count_) | extend dummy=1 | join (counts_tbl | where rid == 1 // current 12h bin | extend dummy=1) on dummy | project maxi, current=count_, diff=count_-maxi but if you want to detect anomaly of the last point I recommend building a time series of counts using make-series and then use series_decompose_anomalies function. Thanks Adi
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