Refrigeration unit safety monitoring and anomaly root cause analysis

A method comprising:

• during an initial time period:
• extracting a set of language concepts from a set of documents comprising refrigeration manuals for a nominal set of refrigeration system types;
• aggregating the set of language concepts into a corpus of textual training data comprising descriptors of:
• characteristics of the nominal set of refrigeration system types;
• anomalous behaviors occurring within refrigeration units of the nominal set of refrigeration system types;
• root causes of anomalous behaviors occurring within refrigeration units of the nominal set of refrigeration system types;
• and
• replacement parts for correcting root causes of anomalous behaviors at refrigeration units of the nominal set of refrigeration system types;
• and
• training a language model on the corpus of textual training data to generate:
• textual descriptions of possible root causes of anomalous behaviors occurring within refrigeration units of a global set of refrigeration system types comprising the nominal set of refrigeration system types;
• and
• textual descriptions of replacement parts for correcting root causes of anomalous behaviors occurring within refrigeration units of the global set of refrigeration system types;
• and
• during a first time period:
• accessing a set of sensor data captured via a set of sensors coupled to a refrigeration unit;
• and
• in response to detecting a first anomalous behavior occurring at the refrigeration unit based on the set of sensor data:
• generating a first textual descriptor of the first anomalous behavior;
• accessing a set of characteristics of the refrigeration unit;
• generating a first text string describing a root cause of the first anomalous behavior based on:
• proximity of the set of characteristics to characteristics of the nominal set of refrigeration system types represented in the language model;
• and
• proximity of the first textual descriptor to troubleshooting descriptions represented in the language model for a subset of analogous refrigeration system types in the nominal set of refrigeration system types;
• generating a second text string describing a set of replacement parts for correcting the first anomalous behavior at the refrigeration unit based on proximity of the first textual descriptor to corrective action descriptions represented in the language model for the subset of analogous refrigeration system types;
• generating a notification comprising the first textual descriptor, the first text string, and the second text string;
• and
• serving the first notification to an operator associated with the refrigeration unit.

A method comprising:

• during an initial time period:
• extracting a set of language concepts from a set of documents describing refrigeration unit management, the set of language concepts comprising descriptions of characteristics of refrigeration units, anomalous behaviors of refrigeration units, and repair of refrigeration units;
• aggregating the set of language concepts into a corpus of textual training data;
• representing the corpus of textual training data as a set of embeddings in an embedding space;
• and
• based on the set of embeddings, training a language model to detect patterns of vocabulary, grammar, and semantics in the corpus of textual training data;
• and
• during an operating period for a refrigeration unit:
• accessing a set of sensor data representing operation of the refrigeration unit;
• and
• in response to detecting an anomalous behavior occurring at the refrigeration unit based on the set of sensor data;
• generating a textual descriptor of the anomalous behavior;
• retrieving a set of characteristics of the refrigeration unit;
• generating a query requesting identification of a root cause of the anomalous behavior in the refrigeration unit and describing the set of characteristics;
• generating a first embedding corresponding to the query and representing semantic relationships of words in the query;
• identifying a first subset of embeddings, in the set of embeddings, proximal the first embedding within the embedding space;
• extracting a subset of language concepts, in the set of language concepts, proximal the first subset of embeddings represented in the embedding space;
• assembling the subset of language concepts into a first text string describing:
• a first root cause of the anomalous behavior;
• and
• a first corrective action for correcting the anomalous behavior at the refrigeration unit;
• generating a first notification comprising the first textual descriptor and the first text string;
• and
• serving the first notification to a user associated with the refrigeration unit.