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Early Detec­tion of Mech­an­ical Issues

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Indus­tri­als  - Early Detec­tion of Mech­an­ical Issues

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Indus­tri­als

In the pro­ject the cus­tomer needed to develop a pre­dict­ive main­ten­ance plat­form using Big Data tech­no­lo­gies to detect fail­ing com­pon­ents before they break, enabling bet­ter visu­al­iz­a­tion and ana­lysis of main­ten­ance issues, con­tact­ing cus­tom­ers for war­ranty replace­ments, and sig­ni­fic­antly redu­cing repair costs. synvert cre­ated a time-depend­ent ML model to pre­dict poten­tial prob­lems with con­fid­ence inter­vals for increased interpretability.

Ini­tial situation

The cus­tomer col­lec­ted a large amount of man­u­fac­tur­ing and main­ten­ance data and was strug­gling to make sense of it.

The vis­ion was to develop a pre­dict­ive main­ten­ance plat­form to save costs by redu­cing expens­ive repairs.

This can be achieved by detect­ing fail­ing com­pon­ents before they break.

Archi­tec­ture

The entire pipeline has been developed using Big Data tech­no­lo­gies (Spark, Hadoop, Python) and is com­pleted with a dash­board for visu­al­iz­a­tion in Tableau.

After the pro­duc­tion rol­lout, he pro­ject has been handed over to the oper­a­tions team.

Gen­er­ated bene­fits

The cus­tomer is able to bet­ter visu­al­ize and ana­lyze main­ten­ance issues.

Mul­tiple cus­tom­ers can be con­tac­ted to replace spe­cific com­pon­ents while still under warranty.

The oper­a­tions teams are also able to identify pat­terns and sig­ni­fic­antly reduce high repair costs.

Ser­vices accom­plished by synvert 

synvert has developed a pre­dic­tion model based on a time-depend­ent train­ing set.

The ML model is able to pre­dict poten­tial prob­lems for a given machine based on many dif­fer­ent char­ac­ter­ist­ics (when it was man­u­fac­tured, when it was sold, com­pon­ents, etc.).

For each pre­dic­tion, a con­fid­ence inter­val for the pre­dic­tion is also provided to increase the inter­pretab­il­ity of the model.

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