Local AI to prepare an equipment failure review

An alarm, a stop and a service action

An engineer prepares a failure review. The log contains an alarm from before the machine stopped, the operator described the stop in a note, and a later service order records a part replacement. The app arranges these records in order and shows the source next to each one. The operator's entry stays an observation attributed to them, and the replacement stays an action from the order.

The order of events does not prove that the alarm pointed to the cause or that the replacement fixed the problem. If the material has no description of how the machine behaved afterwards, the summary includes a question about such a record. The date a note was saved can be later than the event it describes, so when a document does not establish the time, the app shows that uncertainty. Differing accounts from the operator and the service team stay visible for clarification.

What local AI for failure reviews costs and when it pays back

The count covers the time spent searching, reading and compiling the material. What stays is the time to check the output against the source, 25% of today's work in the scenario. We measure both times in a trial on 20 to 100 cases the team has already handled. The scenario assumes 20 reviews a month at 3 hours of gathering material each, which fits a plant with several lines and regular post-failure meetings.

What runs locally and what needs agreeing

If the confidentiality of equipment history requires the material to stay in the plant's environment, a local model is one element of the solution. Ollama distinguishes local and cloud models and describes switching off cloud features. The scope of the app covers the whole flow: fetching documents, preparing the summary and saving it, backups included. Choosing a local model does not settle where the other parts of the app process data.

First check the report in your current system

A maintenance management system, a CMMS, may already link the machine, the tickets and the work orders. If the engineer finds the whole history there and moves to the descriptions easily, a separate app will change little. Sometimes it is enough to share the right report or link existing records. A model is worth testing where an important part of the history sits in free-text notes and separate documents. Observations repeated across many visits are covered in the article on analysing service notes.

Where to start

The first trial covers a failure the team knows well and the material maintenance points to. The engineer compares the timeline with the current report: which documents it reaches faster, what needs correcting and whether the questions point to real gaps. We roll out local models as part of private LLMs, and we build the view for working with sources as an AI app. For the free process review, a description of one case and the places where the material sits is enough. Confidential files stay in the company, and you get a written plan within 2 working days.