A method comprising: receiving a plurality of time series signals generated by a plurality of sensors of one or more machines; characterized in that the method further comprises: for a top-level evaluation range ending at a certain time and for each signal of the plurality of time series signals: identifying a base range transformation window for the signal through an iterative process of range reduction, each iteration corresponding to a level and a level range (704, 706); for each level: transforming the signal into a plurality of digests using the corresponding level range, each digest including a set of values representing signal behavior for a trailing period of time (708, 302); and inputting the digests for each level as pseudo time-series signals to a transformer to generate a transformation block for the level; composing the transformation blocks for the levels into a range digestion block for the certain time, the range digestion block (602, 806) transforming incoming signal data for a time range ending at the certain time into a range digest for the certain time; generating a resolver block from the range digestion block (602, 806) and known condition episodes, the resolver block configured to transform a given range digest for a given time into a condition value for the given or a later time; composing a condition recognition block from the range digestion block and the resolver block; and applying a combination of unsupervised and supervised learning to transform the condition recognition block into a feedback signal applied to the or more machines. receiving a plurality of time series signals generated by a plurality of sensors of one or more machines; characterized in that the method further comprises: for a top-level evaluation range ending at a certain time and for each signal of the plurality of time series signals: identifying a base range transformation window for the signal through an iterative process of range reduction, each iteration corresponding to a level and a level range (704, 706); for each level: transforming the signal into a plurality of digests using the corresponding level range, each digest including a set of values representing signal behavior for a trailing period of time (708, 302); and inputting the digests for each level as pseudo time-series signals to a transformer to generate a transformation block for the level; composing the transformation blocks for the levels into a range digestion block for the certain time, the range digestion block (602, 806) transforming incoming signal data for a time range ending at the certain time into a range digest for the certain time; generating a resolver block from the range digestion block (602, 806) and known condition episodes, the resolver block configured to transform a given range digest for a given time into a condition value for the given or a later time; composing a condition recognition block from the range digestion block and the resolver block; and applying a combination of unsupervised and supervised learning to transform the condition recognition block into a feedback signal applied to the or more machines. identifying a base range transformation window for the signal through an iterative process of range reduction, each iteration corresponding to a level and a level range (704, 706); for each level: transforming the signal into a plurality of digests using the corresponding level range, each digest including a set of values representing signal behavior for a trailing period of time (708, 302); and inputting the digests for each level as pseudo time-series signals to a transformer to generate a transformation block for the level; transforming the signal into a plurality of digests using the corresponding level range, each digest including a set of values representing signal behavior for a trailing period of time (708, 302); and inputting the digests for each level as pseudo time-series signals to a transformer to generate a transformation block for the level; composing the transformation blocks for the levels into a range digestion block for the certain time, the range digestion block (602, 806) transforming incoming signal data for a time range ending at the certain time into a range digest for the certain time; generating a resolver block from the range digestion block (602, 806) and known condition episodes, the resolver block configured to transform a given range digest for a given time into a condition value for the given or a later time; composing a condition recognition block from the range digestion block and the resolver block; and applying a combination of unsupervised and supervised learning to transform the condition recognition block into a feedback signal applied to the or more machines.