Batch　processes　are　very　important　in　most　industries　and　are　used　to　produce　high-value-added　products,　which　causes　their　monitoring　and　control　to　emerge　as　essential　techniques.　Several　multivariate　statistical　analyses,　including　multi-way　principal　component　analysis　(MPCA),　have　been　developed　for　the　monitoring　and　fault　detection　of　batch　processes.　In　this　paper,　an　improved　statistical　batch　monitoring　and　fault　diagnosing　approach　based　on　variable-wise　unfolding　was　proposed　to　overcome　the　drawbacks　of　traditional　MPCA　and　the　AT　method　proposed　by　Aguado.　The　proposed　method　did　not　require　prediction　of　the　future　values　while　the　dynamic　relations　of　data　were　preserved　by　using　time-varying　score　covariance,　and　principal-component-related　variable　residual　statistics　was　introduced　to　replace　SPE-statistics,　thus　avoiding　the　conservation　of　SPE　statistical　test　and　providing　more　explicit　information　about　the　process　conditions.　As　a　result,　the　root　cause　that　violated　the　Hotelling　T2　test　but　still　satisfied　the　SPE　test　could　be　unambiguously　identified,　which　was　impossible　in　the　MPCA.　In　addition,　time-varying　contribution　charts　were　proposed　to　diagnose　anomalous　batch　process.　The　proposed　method　was　applied　to　detecting　and　identifying　faults　in　the　simulation　benchmark　of　fed-batch　penicillin　production.　The　simulation　results　clearly　demonstrated　the　power　and　advantages　of　the　proposed　method　in　comparison　to　the　MPCA　and　AT　method.　©All　Rights　Reserved.